Add Batch 698e4788-022c-47fd-acff-e2a87dc9dba8 data

.gitattributes

@@ -1525,3 +1525,67 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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2024/Occupancy as Set of Points/e88bbcd3-f64f-4fcb-a277-94c463373078_content_list.json

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+        "type": "text",
+        "text": "Occupancy as Set of Points",
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+        "text": "Yiang Shi $^{1,\\star}$ , Tianheng Cheng $^{1,\\star}$ , Qian Zhang $^{2}$ , Wenyu Liu $^{1}$ , and Xinggang Wang $^{1,\\text{忍}}$",
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+    {
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+        "text": "$^{1}$  School of EIC, Huazhong University of Science & Technology",
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+    {
+        "type": "text",
+        "text": "2 Horizon Robotics",
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+    {
+        "type": "text",
+        "text": "Abstract. In this paper, we explore a novel point representation for 3D occupancy prediction from multi-view images, which is named Occupancy as Set of Points. Existing camera-based methods tend to exploit dense volume-based representation to predict the occupancy of the whole scene, making it hard to focus on the special areas or areas out of the perception range. In comparison, we present the Points of Interest (PoIs) to represent the scene and propose OSP, a novel framework for point-based 3D occupancy prediction. Owing to the inherent flexibility of the point-based representation, OSP achieves strong performance compared with existing methods and excels in terms of training and inference adaptability. It extends beyond traditional perception boundaries and can be seamlessly integrated with volume-based methods to significantly enhance their effectiveness. Experiments on the Occ3D-nuScenes occupancy benchmark show that OSP has strong performance and flexibility. Code and models are available at https://github.com/hustvl/osp.",
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+        "type": "text",
+        "text": "Keywords: 3D Occupancy Prediction  $\\cdot$  Autonomous Vehicles  $\\cdot$  Multi-view 3D Perception",
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+        "text": "1 Introduction",
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+        "text": "Holistic 3D scene understanding is crucial for autonomous driving systems, directly affecting the efficiency and accuracy of subsequent tasks. Considering the cost-effectiveness and ease of deployment of cameras compared to other sensors, developing visual-based methods for 3D scene understanding has become a significant and widely researched challenge.",
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+    {
+        "type": "text",
+        "text": "To tackle this challenge, 3D Semantic Scene Completion (SSC) [22] has been proposed and widely studied to jointly infer the geometry and semantics information of the scene from limited observations. The SSC task requires the model to accurately predict the visible locations and complete the information for the invisible locations. Recently, Occ3D [25] introduces a new task definition called 3D occupancy prediction. The main difference between this task and SSC is that 3D occupancy prediction only focuses on the visible areas and is tailored for dynamic scenes.",
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+    {
+        "type": "page_footnote",
+        "text": "* Equal contribution. † Corresponding author: Xinggang Wang (xgwang@hust.edu.cn).",
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+        "img_path": "images/c2db89918190d37d13c18068154ca7442a3a75eef145310947f2ffd263686312.jpg",
+        "image_caption": [
+            "Fig. 1: Comparison between volume-based methods and our method. The volume-based methods, represented by BEVFormer, infers every region within the scene and gets standard occupancy as shown in (a). Our method uses a point-based decoder as shown in (b). Thus it infers the Points of Interest including standard, adaptively sampled, and manually sampled grids as shown in (c)."
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+        "type": "text",
+        "text": "Existing 3D occupancy prediction methods are mostly based on dense BEV methods, e.g., BEVFormer [15], BEVDet [7]. These methods integrate a BEV encoder with an occupancy head to generate the output and enhance BEV perception capabilities for better results. However, they share some common drawbacks. (1) Uniform Sampling: BEV-based methods fail to differentiate between different areas within the same scene, treating them equally. This leads to coarse sampling and hinders dynamic or multi-resolution sampling capabilities. (2) Limited Inference Flexibility: During inference, these methods can only process the entire scene at once. They lack the ability to infer different parts of the scene based on varying downstream tasks or specific practical needs.",
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+    {
+        "type": "text",
+        "text": "Those limitations highlight the need for more flexible 3D occupancy prediction methods that can handle complex scenes while adapting to different inference requirements. In this paper, we propose a novel point-based representation for 3D occupancy prediction. Instead of dividing the scene into uniform grids applied by existing volume-based methods, we propose Points of Interest (PoIs) to view the scene as a collection of points that help in flexibly sampling the scene during both training and inference stages. Fig. 1 compares volume-based and point-based representations. Compared to volume-based representations, our point-based representation has the following advantages: (1) it can accept inputs of any scale and position to make occupancy predictions, including manually designed and adaptively designed input, offering flexibility; (2) it can pay extra attention to certain areas rather than treating all areas equally, enhancing the model's perceptual capabilities.",
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+    {
+        "type": "page_number",
+        "text": "2",
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+    {
+        "type": "header",
+        "text": "Y. Shi, T. Cheng, Q. Zhang, W. Liu, and X. Wang",
+        "bbox": [
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+        "page_idx": 1
+    },
+    {
+        "type": "text",
+        "text": "We introduce Occupancy as Set of Points (OSP), a novel and flexible point-based framework, which is built upon the foundational concept of Points of Interest (PoIs). OSP excels in 3D occupancy prediction and is composed of an image backbone, a 3D positioning encoder, and a decoder, as illustrated in Fig. 2. Central to our methodology is the innovative use of PoIs, which we have categorized into three distinct types to meet diverse needs, thereby significantly enhancing various aspects of our model's performance. Detailed descriptions of these PoIs will be provided in Sec. 3. Notably, PoIs can be designed as needed beyond the three types initially proposed by us.",
+        "bbox": [
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+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "Our method stands out for its strong performance and flexibility. The flexibility enables it to process any arbitrary local scene without necessitating retraining. Additionally, our method can serve as an augmentative plugin module for existing volume-based methods by adaptively resampling areas of low confidence to yield more accurate occupancy predictions. It is also adept at predicting areas beyond the scene. The key contributions of our approach can be summarized as follows:",
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+        "sub_type": "text",
+        "list_items": [
+            "- A novel point-based occupancy representation, established by interacting point queries with 2D image features, enables a comprehensive understanding of 3D scenes.",
+            "- A flexible framework that allows for inference at any area of interest without retraining or sacrificing accuracy and predicts areas beyond the scene.",
+            "- A plugin module that enhances the performance of the volume-based baseline significantly.",
+            "- OSP has obtained strong experiment results, i.e., 39.4 mIoU in the 3D occupancy prediction task on the Occ3D-nuScenes benchmark."
+        ],
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+    {
+        "type": "text",
+        "text": "2 Related Work",
+        "text_level": 1,
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+    },
+    {
+        "type": "text",
+        "text": "2.1 3D Occupancy Prediction",
+        "text_level": 1,
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "3D occupancy prediction, a concept recently defined by Occ3D [25], exhibits notable parallels with Occupancy Grid Mapping (OGM) [20, 24, 27] used in robotics. This task aims to predict the state of each voxel grid in a scene based on a series of sensor inputs. Occ3D establishes two benchmarks leveraging the Waymo Open Dataset [23] and the nuScenes Dataset [2] to facilitate this. In vision-based 3D Occupancy prediction, Occ3D implements camera visibility estimation and creates visibility masks to ensure evaluations are confined to visible areas. It also evaluates various SSC methodologies on its benchmarks, including MonoScene [4], TPVFormer [9], BEVDet [7], OccFormer [32], and BEV-Former [15].",
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+    {
+        "type": "text",
+        "text": "2.2 3D Semantic Scene Completion",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "Scene Semantic Completion (SSC) represents a task closely associated with 3D occupancy prediction. The concept of SSC is initially presented in SSC-Net [22], with a focus on predicting the comprehensive semantic information",
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+    {
+        "type": "header",
+        "text": "Occupancy as Set of Points",
+        "bbox": [
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+        "page_idx": 2
+    },
+    {
+        "type": "page_number",
+        "text": "3",
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+    {
+        "type": "text",
+        "text": "of a scene based on its partially visible regions. Over recent years, the study of SSC has expanded significantly, particularly in the context of small indoor scenes [3,5,11-13,16,30,31]. In recent times, the study of Scene Semantic Completion (SSC) for expansive outdoor environments has gained momentum, particularly following the introduction of the SemanticKITTI dataset [1]. Notably, MonoScene [4] emerges as the first method to apply monocular pure vision-based SSC. In a parallel advancement, OccDepth [19] enhances 2D to 3D feature transformation by incorporating depth data from stereo input. TPVFormer [9] argues against the limitations of single-plane modeling in capturing intricate details, hence it adopts a tri-perspective view (TPV) approach, combining a Bird's Eye View (BEV) with two additional vertical planes. Additionally, Symphonies [10] highlights the significance of instance representation in SSC tasks. While SSC methods can be directly applied to 3D occupancy prediction, two primary distinctions exist: (1) SSC primarily aims to infer the occupancy of non-visible areas based on visible regions, in contrast to 3D occupancy prediction which focuses on visible areas; (2) SSC methods usually target static scenes, whereas 3D occupancy prediction methods are often designed to handle dynamic scenes.",
+        "bbox": [
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+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "Most existing volume-based SSC and 3D occupancy prediction methods are characterized by their dense nature, encompassing inputs and outputs that span the entire scene. Consider the BEVFormer baseline as an example: it segments the scene into uniform BEV grids, failing to distinguish between grids in varying areas. This uniformity restricts the ability of volume-based methods like BEVFormer to sample areas of interest for better performance during training. Besides, if we want to focus on a specific area in the inference stage, volume-based methods are limited to infer the entire scene and then perform post-processing, inevitably leading to increased and unnecessary costs. Moreover, as scene size and voxel resolution increase, the computational demands skyrocket exponentially. In stark contrast, our point-based model introduces much-needed flexibility by focusing on PoIs. Our method facilitates direct inference in specific areas, eliminating the need for post-processing and avoiding additional computational burdens.",
+        "bbox": [
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+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "A point-related SSC method is PointOcc [34], a point cloud-based SSC prediction method using three complementary view planes for efficient point cloud feature modeling and an efficient 2D backbone for processing to reduce computational load, while our method focuses on the flexibility of training and inference.",
+        "bbox": [
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+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "2.3 Camera-based 3D Detection",
+        "text_level": 1,
+        "bbox": [
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+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "Camera-based 3D perception tasks have gained substantial attention in recent research, largely due to the convenience and cost-effectiveness of cameras as data collection sensors. Initial efforts, such as FCOS3D [26] and DETR3D [28], explore the transition from 2D to 3D predictions. BEVFormer [15] represents a significant advancement in this area, transforming images captured from vehicle-mounted cameras into a bird's eye view (BEV) representation. This technique not only enhances a vehicle's environmental understanding but also finds appli",
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+        "page_idx": 3
+    },
+    {
+        "type": "page_number",
+        "text": "4",
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+        "page_idx": 3
+    },
+    {
+        "type": "header",
+        "text": "Y. Shi, T. Cheng, Q. Zhang, W. Liu, and X. Wang",
+        "bbox": [
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+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "cations in various downstream tasks like BEVStereo [14] and BEVDet [7] and extends to 3D occupancy prediction.",
+        "bbox": [
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+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "3D detection tasks are important in camera-based 3D perception and also have a high similarity to 3D occupancy prediction. The primary objective of 3D detection involves estimating the position and dimensions of objects in 3D space. DETR3D [28], drawing inspiration from DETR [33], innovatively combines 3D object queries with image features, incorporating camera intrinsic and extrinsic parameters. PETR [17] and its successor, PETRv2 [18], further refine this approach by addressing the accuracy of reference point sampling and the inclusion of global information, enhancing 3D detection through historical data integration. Compared to object detection, 3D occupancy prediction offers a finer granularity, which is crucial for navigating irregular obstacles or overhanging objects. It is imperative to explore how insights from BEV representation and 3D object detection can inspire innovative solutions tailored to the specific requirements of 3D occupancy prediction.",
+        "bbox": [
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+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "3 Preliminary",
+        "text_level": 1,
+        "bbox": [
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+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "Problem setup. We aim to provide occupancy predictions around the ego-vehicle, given only  $N$  surround-view RGB images. More specifically, we use as input current images denoted by  $\\mathbf{I}_i = \\{I_0,I_1,\\dots,I_N\\}$ , and use as output an occupancy prediction  $\\mathbf{Y}_i\\in \\{c_0,c_1,\\dots,c_M\\}^{H\\times W\\times Z}$  defined in the coordinate of ego-vehicle, where each occupancy prediction is either empty (denoted by  $c_{0}$ ) or occupied by a certain semantic class in  $\\{c_{1},c_{m},\\dots,c_{M}\\}$ . We assume known camera intrinsic parameters  $\\{K_i\\}$  and extrinsic parameters  $\\{[R_i|t_i]\\}$  in each frame. We assume to know whether each area is visible or not by applying a camera visibility mask.",
+        "bbox": [
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+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "Points of Interest. In our model, we innovatively introduce the concept of Points of Interest (PoIs), which is a set of sparse points to represent the 3D scene. PoIs can flexibly represent objects or regions that require extra attention such as pedestrians or regions near ego-vehicles and can be designed as needed in both training and inference phases. We use three types of PoIs and the definitions and functions are introduced as follows:",
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+    {
+        "type": "list",
+        "sub_type": "text",
+        "list_items": [
+            "(1) Standard Grids: By sampling center points of grids in the inference stage and making predictions of standard 3D occupancy grids, our model makes a fair comparison with existing methods and achieves good results.",
+            "(2) Adaptively Sampling: During the training stage, our model adaptively samples points and oversamples points around them to enhance accuracy. Recognizing that volume-based methods uniformly treat all locations, our point-based approach allows for resampling in either areas of special interest or those that are challenging to learn. This adaptively resampling strategy is also used to augment the performance of volume-based methods. Consequently, our method can function as a versatile plugin, seamlessly integrating with and enhancing existing volume-based approaches."
+        ],
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+        "page_idx": 4
+    },
+    {
+        "type": "header",
+        "text": "Occupancy as Set of Points",
+        "bbox": [
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+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "page_number",
+        "text": "5",
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+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "(3) Manually Sampling: Our model excels in its flexibility to sample any area, specifically catering to the unique demands of various downstream tasks. Our model can make predictions of areas beyond the standard perception range by setting PoIs to areas outside the scene manually, e.g. 200 meters away from ego-vehicle, which is a feat unattainable by traditional volume-based methods. This extension not only broadens the scope of inference but also introduces a new dimension to scene understanding. These PoIs are the foundation of our method, offering precision and high flexibility.",
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+        "page_idx": 5
+    },
+    {
+        "type": "image",
+        "img_path": "images/449a3e8aed3cf8a8fcbc855e54bd5977137f27256f6b3f0d14dcfd7ea7ae6ea4.jpg",
+        "image_caption": [
+            "Fig. 2: Overall framework of Occupancy as Set of Points. OSP leverages the Transformer architecture to derive 3D point features from 2D images to make 3D occupancy predictions. Initially, we extract 2D features from multi-view images. Following this, we employ a set of 3D point queries to index these 2D features. The selection of these 3D point queries depends on the Points of Interest (PoIs)."
+        ],
+        "image_footnote": [],
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+    },
+    {
+        "type": "text",
+        "text": "4 Method",
+        "text_level": 1,
+        "bbox": [
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+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "4.1 Overall Architecture",
+        "text_level": 1,
+        "bbox": [
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+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "Fig. 2 shows the overall architecture of the proposed Occupancy as Set of Points. Given the images  $\\mathbf{I} = \\{I_i, i = 1, 2, \\dots, N\\}$  from  $\\mathbf{N}$  views, We feed these images into ResNet [6] to obtain their features.",
+        "bbox": [
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+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "OSP begins with sampling a set of 3D points in the space, which are the initial PoIs. In our experiments, we use center points of grids as the initial PoIs to make a fair comparison with traditional volume-based methods and provide a reliable baseline for performance comparison and evaluation. We sample  $K(K = 8000)$",
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+        "page_idx": 5
+    },
+    {
+        "type": "page_number",
+        "text": "6",
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+        "page_idx": 5
+    },
+    {
+        "type": "header",
+        "text": "Y. Shi, T. Cheng, Q. Zhang, W. Liu, and X. Wang",
+        "bbox": [
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+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "points within the camera's visible region and introduce random perturbations to these points.",
+        "bbox": [
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+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "Then we normalize the 3D points. To these normalized coordinates, we apply sine and cosine functions as a form of positional encoding. This encoded positional information is then utilized to create query position embeddings. In the training phase, within each decoder layer, the query position corresponding to each individual query remains consistent. The 3D coordinates, along with the camera's intrinsic and extrinsic parameters, are used to map these points onto the pixel plane. This mapping process yields corresponding key and value pairs. Subsequently, we employ point cross-attention mechanisms to compute the output. Then we adaptively oversample a group of  $M$  points ( $M = 8000$ ) whose coordinates are calculated by a linear layer and employ group point cross-attention to fuse the features of the additional sampling points.",
+        "bbox": [
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+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "4.2 3D Position Encoder",
+        "text_level": 1,
+        "bbox": [
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+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "After obtaining 3D points by applying PoIs, we first normalize coordinate points using equations as follows:",
+        "bbox": [
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+        "page_idx": 6
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\nx, y, z = \\frac {x - x _ {\\operatorname* {m i n}}}{x _ {\\operatorname* {m a x}} - x _ {\\operatorname* {m i n}}}, \\frac {y - y _ {\\operatorname* {m i n}}}{y _ {\\operatorname* {m a x}} - y _ {\\operatorname* {m i n}}}, \\frac {z - z _ {\\operatorname* {m i n}}}{z _ {\\operatorname* {m a x}} - z _ {\\operatorname* {m i n}}}, \\tag {1}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
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+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "where  $[x_{\\min}, y_{\\min}, z_{\\min}, x_{\\max}, y_{\\max}, z_{\\max}] = [-40m, -40m, -1m, 40m, 40m, 5.4m]$ , are the preset boundaries of the scene. Through sine and cosine functions, we encode the normalized coordinates into high-dimensional positional information. Then we use a small MLP containing two linear layers and one ReLU layer to transform the high-dimensional positional information into learnable embeddings.",
+        "bbox": [
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+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "4.3 Point Decoder",
+        "text_level": 1,
+        "bbox": [
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+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "We used three decoder layers to build our point decoder. Each layer of the decoder contains point cross-attention (PCA) and group point cross-attention (GPCA).",
+        "bbox": [
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+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "The purpose of PCA and GPCA is to integrate position embedding with image features, thereby facilitating a more cohesive representation. Owing to the high efficiency of deformable attention [15], our point decoder mechanisms employ this technique. Consequently, each query within our framework can be updated as follows:",
+        "bbox": [
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+        "page_idx": 6
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\n\\mathrm {D A} (\\mathbf {q}, \\mathbf {p}, \\mathbf {F}) = \\sum_ {s = 1} ^ {N _ {s}} \\mathbf {A} _ {s} \\mathbf {W} _ {s} \\mathbf {F} ^ {\\mathbf {2 D}} (\\mathbf {p} + \\Delta \\mathbf {p} _ {s}), \\tag {2}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
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+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "where  $N_{s}$  represents the number of sampling offsets,  $\\mathbf{A}_s$  represents the learnable attention weights,  $\\mathbf{F}^{2\\mathbf{D}}(\\mathbf{p} + \\varDelta \\mathbf{p}_s)$  represents the image features collected at location  $\\mathbf{p} + \\varDelta \\mathbf{p}_s$  in which  $\\varDelta\\mathbf{p}_s$  represents the offset apply to position  $\\mathbf{p}$ .",
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+        "page_idx": 6
+    },
+    {
+        "type": "header",
+        "text": "Occupancy as Set of Points",
+        "bbox": [
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+        "page_idx": 6
+    },
+    {
+        "type": "page_number",
+        "text": "7",
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+    {
+        "type": "text",
+        "text": "Point cross-attention. The point cross-attention mechanism begins by taking point queries, initially set to zero, and combines them with point position encoding derived from our 3D position encoder. This combination forms the input queries. Subsequently, these queries undergo deformable cross-attention with 2D image features. Given that not all 3D points project onto the image plane, particularly in the context of the six surround views provided by the nuScenes Dataset. Each point is likely to map onto only one or two of these views. We utilize the camera's intrinsic and extrinsic parameters to ascertain which images a given point can map to. This approach ensures that for any specific point, we only consider the features of the image(s) it maps onto, significantly reducing memory consumption.  $\\mathbf{F}^{2D} = \\{\\mathbf{F}_t^{2D},\\mathbf{F}_{t - 1}^{2D},\\ldots \\}$  represents the mapped 2D image features where  $t$  indexes the images. Therefore, the formula of the point cross-attention can be described as follows:",
+        "bbox": [
+            212,
+            146,
+            787,
+            342
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\n\\operatorname {P C A} (\\mathbf {q}, \\mathbf {F} ^ {2 D}) = \\frac {1}{| \\mathcal {V} _ {t} |} \\sum_ {t \\in \\mathcal {V} _ {t}} \\mathrm {D A} (\\mathbf {q}, \\mathcal {P} (\\mathbf {p}, t), \\mathbf {F} _ {t} ^ {2 D}), \\tag {3}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
+            357,
+            345,
+            785,
+            377
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "where  $\\mathcal{V}_t$  represents the hit image,  $t$  indexes the images,  $\\mathcal{P}(\\mathbf{p}, t)$  represents the projection function of input position  $\\mathbf{p}$ .",
+        "bbox": [
+            214,
+            383,
+            785,
+            414
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "Group point cross-attention. The group point cross-attention mechanism is aimed at solving the lack of local context in PCA since each point independently interacts with the image features. We adaptively oversample a group of  $M$  ( $M = 8000$ ) points around our PoIs whose coordinates are calculated by a linear layer. We use the attention obtained from PCA and the 2D image features mapped by our group of points. Therefore, the formula of the group point cross-attention can be described as follows:",
+        "bbox": [
+            214,
+            424,
+            787,
+            529
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\n\\operatorname {G P C A} \\left(\\mathbf {q} _ {\\mathbf {g}}, \\mathbf {F} ^ {2 D}\\right) = \\frac {1}{\\left| \\mathcal {V} _ {t} \\right|} \\sum_ {t \\in \\mathcal {V} _ {t}} \\mathrm {D A} \\left(\\mathbf {q} _ {\\mathbf {g}}, \\mathcal {P} \\left(\\mathbf {p} _ {\\mathbf {g}}, t\\right), \\mathbf {F} _ {t} ^ {2 D}\\right), \\tag {4}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
+            344,
+            532,
+            785,
+            564
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "where  $\\mathbf{q_g}$  represents attention calculated by PCA and  $\\mathbf{p_g}$  represents the input position of the oversampled group of points.",
+        "bbox": [
+            214,
+            570,
+            785,
+            599
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "4.4 Loss Function",
+        "text_level": 1,
+        "bbox": [
+            215,
+            618,
+            377,
+            632
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "We apply class-wise cross-entropy loss and dice loss to this task. The ground truth  $\\hat{\\mathbf{G}}_t \\in \\{c_0, c_1, \\dots, c_M\\}$  represent semantic information of a group of spatial points. The class-wise cross-entropy loss can be computed by:",
+        "bbox": [
+            214,
+            640,
+            785,
+            685
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\n\\mathcal {L} _ {c e} = - \\sum_ {n = 1} ^ {N} \\sum_ {c = c _ {0}} ^ {c _ {M}} w _ {c} \\hat {g} _ {n, c} \\log \\left(\\frac {e ^ {g _ {n , c}}}{\\sum_ {c} e ^ {g _ {n , c}}}\\right), \\tag {5}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
+            367,
+            691,
+            785,
+            732
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "where  $n$  is the index of points,  $N$  is the number of selected points,  $c$  indexes class,  $g_{n,c}$  is the predicted logits for the  $n$ -th point belonging to class  $c$ ,  $w_{c}$  is a weight for each class according to the inverse of the class frequency as in [21]. The dice loss can be computed by:",
+        "bbox": [
+            214,
+            739,
+            787,
+            799
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\n\\mathcal {L} _ {d i c e} = 1 - \\frac {2 \\sum_ {i = 1} ^ {N} p _ {i} g _ {i}}{\\sum_ {i = 1} ^ {N} p _ {i} ^ {2} + \\sum_ {i = 1} ^ {N} g _ {i} ^ {2}}, \\tag {6}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
+            385,
+            805,
+            785,
+            844
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "page_number",
+        "text": "8",
+        "bbox": [
+            217,
+            114,
+            228,
+            126
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "header",
+        "text": "Y. Shi, T. Cheng, Q. Zhang, W. Liu, and X. Wang",
+        "bbox": [
+            271,
+            114,
+            611,
+            128
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "where  $p_i$  is the predicted probability for the point and  $g_i$  is the ground truth binary label for the point. Our final loss is the sum of the two losses:",
+        "bbox": [
+            212,
+            146,
+            782,
+            176
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\n\\mathcal {L} _ {\\text {a l l}} = \\mathcal {L} _ {\\text {d i c e}} + \\mathcal {L} _ {\\text {c e}}. \\tag {7}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
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+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "5 Experiment",
+        "text_level": 1,
+        "bbox": [
+            215,
+            231,
+            366,
+            248
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "5.1 Experimental Setup",
+        "text_level": 1,
+        "bbox": [
+            215,
+            265,
+            428,
+            281
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "Dataset. We perform our experiments on the nuScenes Dataset with annotation provided by Occ3D. Occ3D-nuScenes benchmark is interested in a volume of  $80.0\\mathrm{m}$  to the front and back of the car,  $80.0\\mathrm{m}$  to the left and right side, and  $6.4\\mathrm{m}$  in height. Each sample is divided as a group of 3D voxel grids with a dimension of [200, 200, 16] since each voxel has a size of  $[0.4m, 0.4m, 0.4m]$ . There are 18 categories of occupancy semantics, of which the 18th category is empty and does not participate in the evaluation. Occ3D-nuScenes provides ground-truth semantical voxel grids by aggregate points of the static scenes and moving objects, respectively. Furthermore, Occ3D-nuScenes utilizes ray-casting-based methods to estimate camera visibility and provides camera visibility masks.",
+        "bbox": [
+            212,
+            292,
+            787,
+            444
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "Evaluation metric. For this task, we use mIoU as the evaluation metric, which can be formulated as follows:",
+        "bbox": [
+            212,
+            444,
+            785,
+            473
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\nm I o U = \\frac {1}{C} \\sum_ {c = 1} ^ {C} \\frac {T P _ {c}}{T P _ {c} + F P _ {c} + F N _ {c}}. \\tag {8}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
+            375,
+            484,
+            785,
+            525
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "Following the definition of 3D occupancy prediction, we only evaluate our results in visible regions.",
+        "bbox": [
+            212,
+            536,
+            785,
+            566
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "Implementation details. We input six images of the original size into ResNet10 to obtain image features. Our image backbone is pre-trained on the FCOS3D [26], which is the same backbone used in the BEVFormer baseline. Then the features will be taken by FPN to produce multi-scale feature maps. The dimension of features is  $d = 256$ . We stacked three decoder layers, with point cross-attention and group point cross-attention in each decoder layer. There is a small MLP as our point-based occupancy predictor that projects the 256 feature dimension to the number of classes. We train our model on 8 NVIDIA 3090 GPUs with 24 epochs. We use the AdamW optimizer with a learning rate of  $2 \\times 10^{-4}$  and a weight decay of 0.01. The learning rate of the backbone is 10 times smaller. Besides, we conducted a separate experiment in which we trained our decoder with a frozen BEVFormer baseline backbone using the same setting above and this decoder will be used as a plugin module for the BEVFormer baseline.",
+        "bbox": [
+            212,
+            566,
+            789,
+            763
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "Baseline methods. We compare Occupancy as Set of Points with existing methods replicated by Occ3D on Occ3D-nuScenes benchmark, including MonoScene [4], TPVFormer [9], BEVDet [7], OccFormer [32] and BEVFormer [15]. To make a fair comparison with the baseline, we provide results using BEVFormer by our implementation using the camera visible mask during the training.",
+        "bbox": [
+            212,
+            763,
+            805,
+            840
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "header",
+        "text": "Occupancy as Set of Points",
+        "bbox": [
+            545,
+            114,
+            730,
+            128
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "page_number",
+        "text": "9",
+        "bbox": [
+            774,
+            114,
+            785,
+            126
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "table",
+        "img_path": "images/50c385c2004b4f13d970befbd1a6e55ef338c1968a1530ba8150724e2a76613d.jpg",
+        "table_caption": [
+            "Table 1: 3D Occupancy prediction performance on the Occ3D-nuScenes dataset. * means the performance is achieved by our implementation using the camera mask during training. † means the performance is achieved with a frozen BEVFormer baseline backbone."
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Method</td><td>others</td><td>barrier</td><td>bicycle</td><td>bus</td><td>car</td><td>Cons. Veh</td><td>motorcycle</td><td>pedestrian</td><td>traffic cone</td><td>trailer</td><td>truck</td><td>Dri. Sur</td><td>other flat</td><td>sidewalk</td><td>terrain</td><td>mammade vegetation</td><td>mIoU</td><td></td></tr><tr><td>MonoScene [4]</td><td>1.75</td><td>7.23</td><td>4.26</td><td>4.93</td><td>9.38</td><td>5.67</td><td>3.98</td><td>3.01</td><td>5.90</td><td>4.45</td><td>7.17</td><td>14.91</td><td>6.32</td><td>7.92</td><td>7.43</td><td>1.01</td><td>7.65</td><td>6.06</td></tr><tr><td>TPVFormer [9]</td><td>7.22</td><td>38.90</td><td>13.67</td><td>40.78</td><td>45.90</td><td>17.23</td><td>19.99</td><td>18.85</td><td>14.30</td><td>26.69</td><td>34.17</td><td>55.65</td><td>35.47</td><td>37.55</td><td>30.70</td><td>19.40</td><td>16.78</td><td>27.83</td></tr><tr><td>BEVDet [7]</td><td>4.39</td><td>30.31</td><td>0.23</td><td>32.26</td><td>34.47</td><td>12.97</td><td>10.34</td><td>10.36</td><td>6.26</td><td>8.93</td><td>23.65</td><td>52.27</td><td>24.61</td><td>26.06</td><td>22.31</td><td>15.04</td><td>15.10</td><td>19.38</td></tr><tr><td>OccFormer [32]</td><td>5.94</td><td>30.29</td><td>12.32</td><td>34.40</td><td>39.17</td><td>14.44</td><td>16.45</td><td>17.22</td><td>9.27</td><td>13.90</td><td>26.36</td><td>50.99</td><td>30.96</td><td>34.66</td><td>22.73</td><td>6.76</td><td>6.97</td><td>21.93</td></tr><tr><td>BEVFormer [15]</td><td>5.85</td><td>37.83</td><td>17.87</td><td>40.44</td><td>42.43</td><td>7.36</td><td>23.88</td><td>21.81</td><td>20.98</td><td>22.38</td><td>30.70</td><td>55.35</td><td>28.36</td><td>36.30</td><td>28.06</td><td>20.04</td><td>17.69</td><td>26.88</td></tr><tr><td>CTF-Occ [25]</td><td>8.09</td><td>39.33</td><td>20.56</td><td>38.29</td><td>42.24</td><td>16.93</td><td>24.52</td><td>22.72</td><td>21.05</td><td>22.98</td><td>31.11</td><td>53.33</td><td>33.84</td><td>37.98</td><td>33.23</td><td>20.79</td><td>18.0</td><td>28.53</td></tr><tr><td>SurroundOcc [29]</td><td>8.7</td><td>39.2</td><td>19.7</td><td>41.4</td><td>46.2</td><td>18.7</td><td>20.6</td><td>26.4</td><td>23.3</td><td>27.0</td><td>32.5</td><td>78.0</td><td>38.3</td><td>46.6</td><td>49.6</td><td>36.7</td><td>31.6</td><td>34.4</td></tr><tr><td>OpenOccupancy [27]</td><td>10.4</td><td>45.7</td><td>23.6</td><td>42.4</td><td>49.3</td><td>14.8</td><td>24.6</td><td>27.7</td><td>27.8</td><td>27.6</td><td>33.3</td><td>79.2</td><td>39.8</td><td>47.1</td><td>50.5</td><td>37.7</td><td>31.8</td><td>36.1</td></tr><tr><td>BEVDet-depth [8]</td><td>6.6</td><td>41.2</td><td>7.0</td><td>42.7</td><td>48.4</td><td>18.4</td><td>12.9</td><td>22.0</td><td>18.2</td><td>28.2</td><td>33.2</td><td>80.1</td><td>39.7</td><td>49.1</td><td>52.1</td><td>39.9</td><td>33.8</td><td>33.7</td></tr><tr><td>BEVDet-stereo [8]</td><td>8.6</td><td>45.9</td><td>14.3</td><td>46.0</td><td>51.2</td><td>23.8</td><td>18.9</td><td>24.1</td><td>22.3</td><td>33.6</td><td>37.9</td><td>81.5</td><td>40.5</td><td>52.6</td><td>55.9</td><td>46.9</td><td>41.2</td><td>38.0</td></tr><tr><td>BEVFormer* [15]</td><td>8.54</td><td>46.24</td><td>20.28</td><td>47.46</td><td>53.04</td><td>19.33</td><td>25.39</td><td>26.16</td><td>25.1</td><td>33.45</td><td>37.75</td><td>81.17</td><td>38.64</td><td>49.32</td><td>52.54</td><td>42.73</td><td>36.13</td><td>37.84</td></tr><tr><td>OSP</td><td>11.2</td><td>47.25</td><td>27.06</td><td>47.57</td><td>53.66</td><td>23.21</td><td>29.37</td><td>29.68</td><td>28.41</td><td>32.39</td><td>37.94</td><td>79.35</td><td>41.46</td><td>50.31</td><td>53.23</td><td>40.52</td><td>35.39</td><td>39.41</td></tr><tr><td>\\( OSP^† \\)</td><td>8.87</td><td>46.33</td><td>21.32</td><td>47.51</td><td>53.14</td><td>19.6</td><td>26.12</td><td>26.84</td><td>26.68</td><td>33.67</td><td>37.94</td><td>81.21</td><td>39.13</td><td>49.48</td><td>52.76</td><td>42.73</td><td>36.12</td><td>38.20</td></tr><tr><td>BEVFormer w/ \\( OSP^† \\)</td><td>10.95</td><td>49.0</td><td>27.68</td><td>50.24</td><td>55.99</td><td>22.96</td><td>31.02</td><td>30.91</td><td>30.25</td><td>35.6</td><td>41.23</td><td>82.09</td><td>42.59</td><td>51.9</td><td>55.1</td><td>44.82</td><td>38.17</td><td>41.21</td></tr></table>",
+        "bbox": [
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+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "5.2 Performance",
+        "text_level": 1,
+        "bbox": [
+            215,
+            414,
+            366,
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+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "Evaluation strategy. Our evaluation strategy for the method comprehensively utilized the three types of Points of Interest (PoIs) previously outlined. This approach entailed: (1) Standard Grids. We conducted sampling at the centers of grids, enabling us to benchmark our method's mIoU against traditional volume-based methods. This comparison provided a direct assessment of our method's performance in a standard scenario. (2) Adaptively Sampling. We adaptively oversample in our training phase to enhance our performance. Furthermore, we integrated our method as a complementary tool with volume-based approaches. By adaptively selecting PoIs that need refinement, our method enhances and augments existing volume-based techniques as shown in Fig. 3. (3) Manually Sampling. We manually select points out of the standard perception range and test out the model's perceptual ability.",
+        "bbox": [
+            212,
+            444,
+            787,
+            626
+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "Through these varied and thorough evaluation techniques, we are able to comprehensively assess the performance and flexibility of our method across different scenarios and use cases.",
+        "bbox": [
+            212,
+            627,
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+            671
+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "Standard grids. In Tab. 1, we benchmarked our method against existing camera-based 3D occupancy prediction techniques on the Occ3D-nuScenes benchmark by setting PoIs to standard grids. Our point-based approach achieved a notable improvement, obtains 1.57 mIoU performance gain compared to our implementation of the Bevformer baseline. Notably, our method outperformed the BEVFormer baseline across almost all category metrics, demonstrating a particularly clear advantage in detecting small targets, such as the bicycle  $(20.28 \\rightarrow 27.06)$ , motorcycle  $(25.39 \\rightarrow 29.37)$ , pedestrian  $(26.16 \\rightarrow 29.37)$  and traffic cone  $(25.1 \\rightarrow 28.31)$ . This advantage is mainly because the direct sampling of spatial points is beneficial for feature extraction and mapping of small objects.",
+        "bbox": [
+            212,
+            672,
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+            839
+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "page_number",
+        "text": "10",
+        "bbox": [
+            217,
+            114,
+            235,
+            126
+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "header",
+        "text": "Y. Shi, T. Cheng, Q. Zhang, W. Liu, and X. Wang",
+        "bbox": [
+            271,
+            113,
+            609,
+            128
+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "image",
+        "img_path": "images/36c0684c0386e0388fcbe44d86f3de89e6b0d20b734825c9b09116904f526eff.jpg",
+        "image_caption": [
+            "Fig. 3: Pipeline of refining volume-based methods with OSP. Given RGB images, 2D features are extracted by the frozen image backbone of the volume-based method which in our case is the BEVFormer baseline. We use the volume-based decoder to infer the entire scene, the point decoder to infer our selected 3D points, and combine the results of both using a weighted sum method."
+        ],
+        "image_footnote": [],
+        "bbox": [
+            217,
+            146,
+            779,
+            335
+        ],
+        "page_idx": 10
+    },
+    {
+        "type": "text",
+        "text": "Adaptively Sampling. (1) The results shown in Tab. 4 demonstrate that our adaptively oversampling strategy improved the results from 38.01 to 38.48. (2) In the adaptive refinement experiment, we use our own implementation of the BEVFormer baseline as a representative of volume-based methods and enhance it with our method using different scales of PoIs by adaptively selecting locations with low confidence. Our model is trained with the frozen BEVFormer baseline backbone. The experimental results, as shown in Tab. 3, confirm that our plugin model significantly enhances BEVformer's performance. Notably, the BEVFormer w/ OSP results surpass those obtained from the two models operating independently. Furthermore, as we increase the size of the refined scene, the mIoU scores of BEVFormer w/ OSP also exhibit marked improvements, indicating that our refinement approach is very effective across various areas.",
+        "bbox": [
+            212,
+            507,
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+        ],
+        "page_idx": 10
+    },
+    {
+        "type": "text",
+        "text": "Manually Sampling. The Occ3D-nuScenes dataset offers annotations across an  $[80m\\times 80m]$  range. We manually set the points and annotations within a smaller  $[60m\\times 60m]$  area and set PoIs to points out of it. To comprehensively evaluate our method, we conducted assessments across three distinct ranges: the standard  $[60m\\times 60m]$  range, the full dataset range of  $[80m\\times 80m]$ , and an intermediate-range spanning from  $60m$  to  $80m$ . Results shown in Tab. 2 demonstrate the capability of our method to make predictions at manually selected locations which is outside the predefined range in this case.",
+        "bbox": [
+            212,
+            719,
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+            840
+        ],
+        "page_idx": 10
+    },
+    {
+        "type": "header",
+        "text": "Occupancy as Set of Points",
+        "bbox": [
+            547,
+            114,
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+            128
+        ],
+        "page_idx": 10
+    },
+    {
+        "type": "page_number",
+        "text": "11",
+        "bbox": [
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+        "type": "table",
+        "img_path": "images/8bbfee116e7cf4b097259fec1ed671207cf93a6613fd5e32ce76c6115962dc49.jpg",
+        "table_caption": [
+            "Table 2: Predict regions beyond the scene. The model was trained on scenes within a  $60m \\times 60m$  range, and evaluations were conducted on the training scenes, the entire size of the scene, and the distance from  $60m$  to  $80m$ ."
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Method</td><td>Scene</td><td>Range</td><td>mIoU</td></tr><tr><td>OSP</td><td>Standard</td><td>0m ~ 60m</td><td>42.29</td></tr><tr><td>OSP</td><td>Large</td><td>0m ~ 80m</td><td>29.00</td></tr><tr><td>OSP</td><td>Beyond</td><td>60m ~ 80m</td><td>23.00</td></tr></table>",
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+        "type": "table",
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+        "table_caption": [
+            "Table 3: Refine BEVFormer with our decoder.  $\\dagger$  means our model is trained with a frozen BEVFormer baseline backbone. Refine scale refers to an area defined by the length multiplied by the width, centered around the self-vehicle. The entire scene spans a scale of  $80m\\times 80m$ . As the refine scale increases, the mIoU also gradually increases."
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Method</td><td>Refine Scale</td><td>mIoU</td></tr><tr><td>BEVFormer</td><td>-</td><td>37.84</td></tr><tr><td>\\( OSP^† \\)</td><td>-</td><td>38.20</td></tr><tr><td>BEVFormer w/ OSP</td><td>20m × 20m</td><td>38.20</td></tr><tr><td>BEVFormer w/ OSP</td><td>40m × 40m</td><td>39.35</td></tr><tr><td>BEVFormer w/ OSP</td><td>50m × 50m</td><td>40.32</td></tr><tr><td>BEVFormer w/ OSP</td><td>80m × 80m</td><td>41.21</td></tr></table>",
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+    {
+        "type": "text",
+        "text": "5.3 Ablation studies",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "Model architecture. We conducted ablation experiments on the model structure, particularly exploring variations in the number of transformer layers and the adaptively oversampling strategy. The results of these experiments are presented in Tab. 4. Even without an oversampling strategy and with just a 3-layer transformer, our method's baseline already surpasses the BEVFormer baseline, advancing from 37.84 to 38.01 in mIoU. The oversampling strategy further amplifies this improvement by enhancing the connections between spatial points, culminating in an increased mIoU of 38.48.",
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+        "table_caption": [
+            "Table 4: Model architecture. To make a fair comparison with the baseline, we set the number of layers to three. The oversampling strategy brings  $0.47\\mathrm{mIoU}$ ."
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Layer Num</td><td>Oversample</td><td>mIoU</td></tr><tr><td>1</td><td>-</td><td>36.76</td></tr><tr><td>3</td><td>-</td><td>38.01</td></tr><tr><td>3</td><td>✓</td><td>38.48</td></tr></table>",
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+        "type": "page_number",
+        "text": "12",
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+    {
+        "type": "header",
+        "text": "Y. Shi, T. Cheng, Q. Zhang, W. Liu, and X. Wang",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "2D image features. The performance of our method is heavily reliant on the quality of image features. To elucidate this dependency, we conducted ablation studies focusing on the output from the model's 'neck' component. These experiments demonstrated the substantial impact of using a multi-scale output from the FPN. This architectural choice boosts the model's performance, as evidenced by the increase in mIoU from 38.67 to 39.41 as shown in Tab. 5.",
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+    {
+        "type": "text",
+        "text": "Grid center sampling method. Our ablation experiments also investigated the technique for the grid center sampling method during training. While initially using grid center points as coordinates, we introduced a variation by adding random perturbations. The results, as detailed in Tab. 5, indicate the introduction of this disturbance to the grid center points elevates the mIoU to 38.67.",
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+        "type": "table",
+        "img_path": "images/e2ff541d2001225b1ba0d73a3841ac34d69ad963bde736e3d933586e8f5b2dd6.jpg",
+        "table_caption": [
+            "Table 5: 2D image features and point perturbation. Increasing the multi-scale feature maps of images from 2 to 4 brings  $0.74\\mathrm{mIoU}$ . We randomly applied perturbations of  $0.1m$  to the coordinates of each point in three directions. This strategy brings  $0.19\\mathrm{mIoU}$"
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Multi-scale Images Features</td><td>Point Perturbation</td><td>mIoU</td></tr><tr><td>2</td><td>-</td><td>38.48</td></tr><tr><td>2</td><td>✓</td><td>38.67</td></tr><tr><td>4</td><td>✓</td><td>39.41</td></tr></table>",
+        "bbox": [
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+        "page_idx": 12
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+    {
+        "type": "text",
+        "text": "Loss design. The task of 3D occupancy prediction bears similarities to segmentation tasks. In the context of the Occ3D-nuScenes dataset, there is a notable issue of class imbalance. To address this challenge, we opted to incorporate dice loss into our optimization strategy. The effectiveness of this choice is evident in the results presented in Tab. 6, which demonstrate that the integration of dice loss benefits our method.",
+        "bbox": [
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+        "page_idx": 12
+    },
+    {
+        "type": "table",
+        "img_path": "images/9dc705170cae3f24b6e9187006045f42d77cba9a825d974a7bf4d6f2240e600e.jpg",
+        "table_caption": [
+            "Table 6: Loss design. Incorporating dice loss for optimization brings  $0.53\\mathrm{mIoU}$ ."
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Dice Loss</td><td>mIoU</td></tr><tr><td>-</td><td>38.88</td></tr><tr><td>✓</td><td>39.41</td></tr></table>",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "Adaptive point sampling. During the inference process, we apply adaptive sampling (adaptively select points with high uncertainty) in BEVFormer w/ OSP and OSP. Adaptive sampling can reduce computational burden while maintaining good performance as shown in Tab. 7. BEVFormer w/ OSP using adaptive sampling means we refine BEVFormer by adaptively selecting points with high uncertainty (around  $20\\%$ ). OSP using adaptive sampling means we only forward",
+        "bbox": [
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+        "page_idx": 12
+    },
+    {
+        "type": "header",
+        "text": "Occupancy as Set of Points",
+        "bbox": [
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+        ],
+        "page_idx": 12
+    },
+    {
+        "type": "page_number",
+        "text": "13",
+        "bbox": [
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+        "page_idx": 12
+    },
+    {
+        "type": "table",
+        "img_path": "images/2a3dbc240ab4eda7f604e8326eb92905785f7c7805511f3d6f5385d74a92d5d2.jpg",
+        "table_caption": [
+            "Table 7: Adaptability of OSP. 'rel' denotes relative."
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Method</td><td>Adaptive Sampling</td><td>Rel. Computation</td><td>mIoU</td></tr><tr><td>BEVFormer</td><td>-</td><td>-</td><td>37.84</td></tr><tr><td>BEVFormer w/ OSP</td><td>-</td><td>1.0×</td><td>39.35</td></tr><tr><td>BEVFormer w/ OSP</td><td>✓</td><td>0.8×</td><td>39.22</td></tr><tr><td>OSP</td><td>-</td><td>1.0×</td><td>39.41</td></tr><tr><td>OSP</td><td>✓</td><td>0.93×</td><td>39.08</td></tr></table>",
+        "bbox": [
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+        "page_idx": 13
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+    {
+        "type": "text",
+        "text": "points with low confidence to the next decoder layer, while directly outputting the results for points with high confidence in the decoder. Points of high uncertainty are defined as those with a confidence score less than a threshold of 0.9 after softmax.",
+        "bbox": [
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+        "page_idx": 13
+    },
+    {
+        "type": "image",
+        "img_path": "images/2daec395d6a850edda3f9b6943bfd379555023b28f066d76d77de987a8c88d00.jpg",
+        "image_caption": [
+            "Fig. 4: Visualization of our results. Our visualization results have voids compared to the ground truth ego-vehicle position, which is due to the surrounding view having gaps near the ego-vehicle."
+        ],
+        "image_footnote": [],
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "6 Conclusion",
+        "text_level": 1,
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "In this work, we present a novel perspective on 3D scene representation, viewing it through a set of points. We introduce the innovative concept of Points of Interest (PoIs), which significantly advances the flexibility in scene representation. Building upon the foundation laid by PoIs, we develop a highly adaptable, point-based 3D occupancy prediction framework, named OSP. We validate the strong performance and flexibility of OSP on the Occ3D-nuScenes benchmark. Our work not only contributes to the field of 3D occupancy prediction but also paves the way for more dynamic and adaptable methods in 3D scene analysis.",
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+    {
+        "type": "page_number",
+        "text": "14",
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+    {
+        "type": "header",
+        "text": "Y. Shi, T. Cheng, Q. Zhang, W. Liu, and X. Wang",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "Acknowledgements",
+        "text_level": 1,
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+    },
+    {
+        "type": "text",
+        "text": "This work was supported by National Natural Science Foundation of China (No. 62376102) and the Agricultural Science and Technology Independent Innovation Foundation of Jiangsu Province, China, No. CX(22)2031.",
+        "bbox": [
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+        "page_idx": 14
+    },
+    {
+        "type": "text",
+        "text": "References",
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+        "bbox": [
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+        "page_idx": 14
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+    {
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+        ],
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+        "page_idx": 15
+    },
+    {
+        "type": "list",
+        "sub_type": "ref_text",
+        "list_items": [
+            "32. Zhang, Y., Zhu, Z., Du, D.: Occformer: Dual-path transformer for vision-based 3d semantic occupancy prediction. arXiv preprint arXiv:2304.05316 (2023) 3, 9, 10",
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+        ],
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+            ],
+            "angle": 0,
+            "content": "Abstract. In this paper, we explore a novel point representation for 3D occupancy prediction from multi-view images, which is named Occupancy as Set of Points. Existing camera-based methods tend to exploit dense volume-based representation to predict the occupancy of the whole scene, making it hard to focus on the special areas or areas out of the perception range. In comparison, we present the Points of Interest (PoIs) to represent the scene and propose OSP, a novel framework for point-based 3D occupancy prediction. Owing to the inherent flexibility of the point-based representation, OSP achieves strong performance compared with existing methods and excels in terms of training and inference adaptability. It extends beyond traditional perception boundaries and can be seamlessly integrated with volume-based methods to significantly enhance their effectiveness. Experiments on the Occ3D-nuScenes occupancy benchmark show that OSP has strong performance and flexibility. Code and models are available at https://github.com/hustvl/osp."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.261,
+                0.527,
+                0.741,
+                0.556
+            ],
+            "angle": 0,
+            "content": "Keywords: 3D Occupancy Prediction \\(\\cdot\\) Autonomous Vehicles \\(\\cdot\\) Multi-view 3D Perception"
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.216,
+                0.579,
+                0.377,
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+            ],
+            "angle": 0,
+            "content": "1 Introduction"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.608,
+                0.787,
+                0.683
+            ],
+            "angle": 0,
+            "content": "Holistic 3D scene understanding is crucial for autonomous driving systems, directly affecting the efficiency and accuracy of subsequent tasks. Considering the cost-effectiveness and ease of deployment of cameras compared to other sensors, developing visual-based methods for 3D scene understanding has become a significant and widely researched challenge."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.684,
+                0.788,
+                0.804
+            ],
+            "angle": 0,
+            "content": "To tackle this challenge, 3D Semantic Scene Completion (SSC) [22] has been proposed and widely studied to jointly infer the geometry and semantics information of the scene from limited observations. The SSC task requires the model to accurately predict the visible locations and complete the information for the invisible locations. Recently, Occ3D [25] introduces a new task definition called 3D occupancy prediction. The main difference between this task and SSC is that 3D occupancy prediction only focuses on the visible areas and is tailored for dynamic scenes."
+        },
+        {
+            "type": "page_footnote",
+            "bbox": [
+                0.232,
+                0.812,
+                0.784,
+                0.84
+            ],
+            "angle": 0,
+            "content": "* Equal contribution. † Corresponding author: Xinggang Wang (xgwang@hust.edu.cn)."
+        }
+    ],
+    [
+        {
+            "type": "page_number",
+            "bbox": [
+                0.218,
+                0.116,
+                0.23,
+                0.127
+            ],
+            "angle": 0,
+            "content": "2"
+        },
+        {
+            "type": "header",
+            "bbox": [
+                0.272,
+                0.115,
+                0.612,
+                0.13
+            ],
+            "angle": 0,
+            "content": "Y. Shi, T. Cheng, Q. Zhang, W. Liu, and X. Wang"
+        },
+        {
+            "type": "image",
+            "bbox": [
+                0.219,
+                0.15,
+                0.571,
+                0.362
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "image",
+            "bbox": [
+                0.576,
+                0.15,
+                0.781,
+                0.362
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "image_caption",
+            "bbox": [
+                0.214,
+                0.376,
+                0.788,
+                0.447
+            ],
+            "angle": 0,
+            "content": "Fig. 1: Comparison between volume-based methods and our method. The volume-based methods, represented by BEVFormer, infers every region within the scene and gets standard occupancy as shown in (a). Our method uses a point-based decoder as shown in (b). Thus it infers the Points of Interest including standard, adaptively sampled, and manually sampled grids as shown in (c)."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.487,
+                0.788,
+                0.639
+            ],
+            "angle": 0,
+            "content": "Existing 3D occupancy prediction methods are mostly based on dense BEV methods, e.g., BEVFormer [15], BEVDet [7]. These methods integrate a BEV encoder with an occupancy head to generate the output and enhance BEV perception capabilities for better results. However, they share some common drawbacks. (1) Uniform Sampling: BEV-based methods fail to differentiate between different areas within the same scene, treating them equally. This leads to coarse sampling and hinders dynamic or multi-resolution sampling capabilities. (2) Limited Inference Flexibility: During inference, these methods can only process the entire scene at once. They lack the ability to infer different parts of the scene based on varying downstream tasks or specific practical needs."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.644,
+                0.788,
+                0.841
+            ],
+            "angle": 0,
+            "content": "Those limitations highlight the need for more flexible 3D occupancy prediction methods that can handle complex scenes while adapting to different inference requirements. In this paper, we propose a novel point-based representation for 3D occupancy prediction. Instead of dividing the scene into uniform grids applied by existing volume-based methods, we propose Points of Interest (PoIs) to view the scene as a collection of points that help in flexibly sampling the scene during both training and inference stages. Fig. 1 compares volume-based and point-based representations. Compared to volume-based representations, our point-based representation has the following advantages: (1) it can accept inputs of any scale and position to make occupancy predictions, including manually designed and adaptively designed input, offering flexibility; (2) it can pay extra attention to certain areas rather than treating all areas equally, enhancing the model's perceptual capabilities."
+        }
+    ],
+    [
+        {
+            "type": "header",
+            "bbox": [
+                0.548,
+                0.115,
+                0.732,
+                0.129
+            ],
+            "angle": 0,
+            "content": "Occupancy as Set of Points"
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.775,
+                0.117,
+                0.785,
+                0.127
+            ],
+            "angle": 0,
+            "content": "3"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.147,
+                0.788,
+                0.283
+            ],
+            "angle": 0,
+            "content": "We introduce Occupancy as Set of Points (OSP), a novel and flexible point-based framework, which is built upon the foundational concept of Points of Interest (PoIs). OSP excels in 3D occupancy prediction and is composed of an image backbone, a 3D positioning encoder, and a decoder, as illustrated in Fig. 2. Central to our methodology is the innovative use of PoIs, which we have categorized into three distinct types to meet diverse needs, thereby significantly enhancing various aspects of our model's performance. Detailed descriptions of these PoIs will be provided in Sec. 3. Notably, PoIs can be designed as needed beyond the three types initially proposed by us."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.283,
+                0.788,
+                0.388
+            ],
+            "angle": 0,
+            "content": "Our method stands out for its strong performance and flexibility. The flexibility enables it to process any arbitrary local scene without necessitating retraining. Additionally, our method can serve as an augmentative plugin module for existing volume-based methods by adaptively resampling areas of low confidence to yield more accurate occupancy predictions. It is also adept at predicting areas beyond the scene. The key contributions of our approach can be summarized as follows:"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.226,
+                0.396,
+                0.784,
+                0.437
+            ],
+            "angle": 0,
+            "content": "- A novel point-based occupancy representation, established by interacting point queries with 2D image features, enables a comprehensive understanding of 3D scenes."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.225,
+                0.44,
+                0.784,
+                0.468
+            ],
+            "angle": 0,
+            "content": "- A flexible framework that allows for inference at any area of interest without retraining or sacrificing accuracy and predicts areas beyond the scene."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.225,
+                0.47,
+                0.784,
+                0.498
+            ],
+            "angle": 0,
+            "content": "- A plugin module that enhances the performance of the volume-based baseline significantly."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.225,
+                0.499,
+                0.784,
+                0.527
+            ],
+            "angle": 0,
+            "content": "- OSP has obtained strong experiment results, i.e., 39.4 mIoU in the 3D occupancy prediction task on the Occ3D-nuScenes benchmark."
+        },
+        {
+            "type": "list",
+            "bbox": [
+                0.225,
+                0.396,
+                0.784,
+                0.527
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.215,
+                0.55,
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+                0.566
+            ],
+            "angle": 0,
+            "content": "2 Related Work"
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.215,
+                0.579,
+                0.475,
+                0.594
+            ],
+            "angle": 0,
+            "content": "2.1 3D Occupancy Prediction"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.602,
+                0.788,
+                0.753
+            ],
+            "angle": 0,
+            "content": "3D occupancy prediction, a concept recently defined by Occ3D [25], exhibits notable parallels with Occupancy Grid Mapping (OGM) [20, 24, 27] used in robotics. This task aims to predict the state of each voxel grid in a scene based on a series of sensor inputs. Occ3D establishes two benchmarks leveraging the Waymo Open Dataset [23] and the nuScenes Dataset [2] to facilitate this. In vision-based 3D Occupancy prediction, Occ3D implements camera visibility estimation and creates visibility masks to ensure evaluations are confined to visible areas. It also evaluates various SSC methodologies on its benchmarks, including MonoScene [4], TPVFormer [9], BEVDet [7], OccFormer [32], and BEV-Former [15]."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.215,
+                0.773,
+                0.522,
+                0.789
+            ],
+            "angle": 0,
+            "content": "2.2 3D Semantic Scene Completion"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.795,
+                0.787,
+                0.842
+            ],
+            "angle": 0,
+            "content": "Scene Semantic Completion (SSC) represents a task closely associated with 3D occupancy prediction. The concept of SSC is initially presented in SSC-Net [22], with a focus on predicting the comprehensive semantic information"
+        }
+    ],
+    [
+        {
+            "type": "page_number",
+            "bbox": [
+                0.218,
+                0.116,
+                0.23,
+                0.127
+            ],
+            "angle": 0,
+            "content": "4"
+        },
+        {
+            "type": "header",
+            "bbox": [
+                0.272,
+                0.115,
+                0.612,
+                0.129
+            ],
+            "angle": 0,
+            "content": "Y. Shi, T. Cheng, Q. Zhang, W. Liu, and X. Wang"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.147,
+                0.788,
+                0.403
+            ],
+            "angle": 0,
+            "content": "of a scene based on its partially visible regions. Over recent years, the study of SSC has expanded significantly, particularly in the context of small indoor scenes [3,5,11-13,16,30,31]. In recent times, the study of Scene Semantic Completion (SSC) for expansive outdoor environments has gained momentum, particularly following the introduction of the SemanticKITTI dataset [1]. Notably, MonoScene [4] emerges as the first method to apply monocular pure vision-based SSC. In a parallel advancement, OccDepth [19] enhances 2D to 3D feature transformation by incorporating depth data from stereo input. TPVFormer [9] argues against the limitations of single-plane modeling in capturing intricate details, hence it adopts a tri-perspective view (TPV) approach, combining a Bird's Eye View (BEV) with two additional vertical planes. Additionally, Symphonies [10] highlights the significance of instance representation in SSC tasks. While SSC methods can be directly applied to 3D occupancy prediction, two primary distinctions exist: (1) SSC primarily aims to infer the occupancy of non-visible areas based on visible regions, in contrast to 3D occupancy prediction which focuses on visible areas; (2) SSC methods usually target static scenes, whereas 3D occupancy prediction methods are often designed to handle dynamic scenes."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.405,
+                0.788,
+                0.614
+            ],
+            "angle": 0,
+            "content": "Most existing volume-based SSC and 3D occupancy prediction methods are characterized by their dense nature, encompassing inputs and outputs that span the entire scene. Consider the BEVFormer baseline as an example: it segments the scene into uniform BEV grids, failing to distinguish between grids in varying areas. This uniformity restricts the ability of volume-based methods like BEVFormer to sample areas of interest for better performance during training. Besides, if we want to focus on a specific area in the inference stage, volume-based methods are limited to infer the entire scene and then perform post-processing, inevitably leading to increased and unnecessary costs. Moreover, as scene size and voxel resolution increase, the computational demands skyrocket exponentially. In stark contrast, our point-based model introduces much-needed flexibility by focusing on PoIs. Our method facilitates direct inference in specific areas, eliminating the need for post-processing and avoiding additional computational burdens."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.617,
+                0.788,
+                0.678
+            ],
+            "angle": 0,
+            "content": "A point-related SSC method is PointOcc [34], a point cloud-based SSC prediction method using three complementary view planes for efficient point cloud feature modeling and an efficient 2D backbone for processing to reduce computational load, while our method focuses on the flexibility of training and inference."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.216,
+                0.705,
+                0.496,
+                0.719
+            ],
+            "angle": 0,
+            "content": "2.3 Camera-based 3D Detection"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.735,
+                0.788,
+                0.841
+            ],
+            "angle": 0,
+            "content": "Camera-based 3D perception tasks have gained substantial attention in recent research, largely due to the convenience and cost-effectiveness of cameras as data collection sensors. Initial efforts, such as FCOS3D [26] and DETR3D [28], explore the transition from 2D to 3D predictions. BEVFormer [15] represents a significant advancement in this area, transforming images captured from vehicle-mounted cameras into a bird's eye view (BEV) representation. This technique not only enhances a vehicle's environmental understanding but also finds appli"
+        }
+    ],
+    [
+        {
+            "type": "header",
+            "bbox": [
+                0.548,
+                0.115,
+                0.732,
+                0.129
+            ],
+            "angle": 0,
+            "content": "Occupancy as Set of Points"
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.775,
+                0.117,
+                0.785,
+                0.127
+            ],
+            "angle": 0,
+            "content": "5"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.146,
+                0.784,
+                0.177
+            ],
+            "angle": 0,
+            "content": "cations in various downstream tasks like BEVStereo [14] and BEVDet [7] and extends to 3D occupancy prediction."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.179,
+                0.788,
+                0.376
+            ],
+            "angle": 0,
+            "content": "3D detection tasks are important in camera-based 3D perception and also have a high similarity to 3D occupancy prediction. The primary objective of 3D detection involves estimating the position and dimensions of objects in 3D space. DETR3D [28], drawing inspiration from DETR [33], innovatively combines 3D object queries with image features, incorporating camera intrinsic and extrinsic parameters. PETR [17] and its successor, PETRv2 [18], further refine this approach by addressing the accuracy of reference point sampling and the inclusion of global information, enhancing 3D detection through historical data integration. Compared to object detection, 3D occupancy prediction offers a finer granularity, which is crucial for navigating irregular obstacles or overhanging objects. It is imperative to explore how insights from BEV representation and 3D object detection can inspire innovative solutions tailored to the specific requirements of 3D occupancy prediction."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.216,
+                0.405,
+                0.369,
+                0.423
+            ],
+            "angle": 0,
+            "content": "3 Preliminary"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.443,
+                0.788,
+                0.579
+            ],
+            "angle": 0,
+            "content": "Problem setup. We aim to provide occupancy predictions around the ego-vehicle, given only \\(N\\) surround-view RGB images. More specifically, we use as input current images denoted by \\(\\mathbf{I}_i = \\{I_0,I_1,\\dots,I_N\\}\\), and use as output an occupancy prediction \\(\\mathbf{Y}_i\\in \\{c_0,c_1,\\dots,c_M\\}^{H\\times W\\times Z}\\) defined in the coordinate of ego-vehicle, where each occupancy prediction is either empty (denoted by \\(c_{0}\\)) or occupied by a certain semantic class in \\(\\{c_{1},c_{m},\\dots,c_{M}\\}\\). We assume known camera intrinsic parameters \\(\\{K_i\\}\\) and extrinsic parameters \\(\\{[R_i|t_i]\\}\\) in each frame. We assume to know whether each area is visible or not by applying a camera visibility mask."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.581,
+                0.788,
+                0.67
+            ],
+            "angle": 0,
+            "content": "Points of Interest. In our model, we innovatively introduce the concept of Points of Interest (PoIs), which is a set of sparse points to represent the 3D scene. PoIs can flexibly represent objects or regions that require extra attention such as pedestrians or regions near ego-vehicles and can be designed as needed in both training and inference phases. We use three types of PoIs and the definitions and functions are introduced as follows:"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.673,
+                0.787,
+                0.717
+            ],
+            "angle": 0,
+            "content": "(1) Standard Grids: By sampling center points of grids in the inference stage and making predictions of standard 3D occupancy grids, our model makes a fair comparison with existing methods and achieves good results."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.72,
+                0.788,
+                0.84
+            ],
+            "angle": 0,
+            "content": "(2) Adaptively Sampling: During the training stage, our model adaptively samples points and oversamples points around them to enhance accuracy. Recognizing that volume-based methods uniformly treat all locations, our point-based approach allows for resampling in either areas of special interest or those that are challenging to learn. This adaptively resampling strategy is also used to augment the performance of volume-based methods. Consequently, our method can function as a versatile plugin, seamlessly integrating with and enhancing existing volume-based approaches."
+        },
+        {
+            "type": "list",
+            "bbox": [
+                0.214,
+                0.673,
+                0.788,
+                0.84
+            ],
+            "angle": 0,
+            "content": null
+        }
+    ],
+    [
+        {
+            "type": "page_number",
+            "bbox": [
+                0.218,
+                0.116,
+                0.23,
+                0.127
+            ],
+            "angle": 0,
+            "content": "6"
+        },
+        {
+            "type": "header",
+            "bbox": [
+                0.271,
+                0.115,
+                0.612,
+                0.13
+            ],
+            "angle": 0,
+            "content": "Y. Shi, T. Cheng, Q. Zhang, W. Liu, and X. Wang"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.147,
+                0.788,
+                0.269
+            ],
+            "angle": 0,
+            "content": "(3) Manually Sampling: Our model excels in its flexibility to sample any area, specifically catering to the unique demands of various downstream tasks. Our model can make predictions of areas beyond the standard perception range by setting PoIs to areas outside the scene manually, e.g. 200 meters away from ego-vehicle, which is a feat unattainable by traditional volume-based methods. This extension not only broadens the scope of inference but also introduces a new dimension to scene understanding. These PoIs are the foundation of our method, offering precision and high flexibility."
+        },
+        {
+            "type": "image",
+            "bbox": [
+                0.226,
+                0.298,
+                0.784,
+                0.534
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "image_caption",
+            "bbox": [
+                0.214,
+                0.546,
+                0.788,
+                0.618
+            ],
+            "angle": 0,
+            "content": "Fig. 2: Overall framework of Occupancy as Set of Points. OSP leverages the Transformer architecture to derive 3D point features from 2D images to make 3D occupancy predictions. Initially, we extract 2D features from multi-view images. Following this, we employ a set of 3D point queries to index these 2D features. The selection of these 3D point queries depends on the Points of Interest (PoIs)."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.216,
+                0.673,
+                0.331,
+                0.687
+            ],
+            "angle": 0,
+            "content": "4 Method"
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.216,
+                0.707,
+                0.433,
+                0.721
+            ],
+            "angle": 0,
+            "content": "4.1 Overall Architecture"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.734,
+                0.788,
+                0.779
+            ],
+            "angle": 0,
+            "content": "Fig. 2 shows the overall architecture of the proposed Occupancy as Set of Points. Given the images \\(\\mathbf{I} = \\{I_i, i = 1, 2, \\dots, N\\}\\) from \\(\\mathbf{N}\\) views, We feed these images into ResNet [6] to obtain their features."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.78,
+                0.788,
+                0.841
+            ],
+            "angle": 0,
+            "content": "OSP begins with sampling a set of 3D points in the space, which are the initial PoIs. In our experiments, we use center points of grids as the initial PoIs to make a fair comparison with traditional volume-based methods and provide a reliable baseline for performance comparison and evaluation. We sample \\( K(K = 8000) \\)"
+        }
+    ],
+    [
+        {
+            "type": "header",
+            "bbox": [
+                0.547,
+                0.115,
+                0.732,
+                0.129
+            ],
+            "angle": 0,
+            "content": "Occupancy as Set of Points"
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.775,
+                0.116,
+                0.787,
+                0.127
+            ],
+            "angle": 0,
+            "content": "7"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.147,
+                0.784,
+                0.176
+            ],
+            "angle": 0,
+            "content": "points within the camera's visible region and introduce random perturbations to these points."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.178,
+                0.788,
+                0.344
+            ],
+            "angle": 0,
+            "content": "Then we normalize the 3D points. To these normalized coordinates, we apply sine and cosine functions as a form of positional encoding. This encoded positional information is then utilized to create query position embeddings. In the training phase, within each decoder layer, the query position corresponding to each individual query remains consistent. The 3D coordinates, along with the camera's intrinsic and extrinsic parameters, are used to map these points onto the pixel plane. This mapping process yields corresponding key and value pairs. Subsequently, we employ point cross-attention mechanisms to compute the output. Then we adaptively oversample a group of \\( M \\) points (\\( M = 8000 \\)) whose coordinates are calculated by a linear layer and employ group point cross-attention to fuse the features of the additional sampling points."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.216,
+                0.366,
+                0.434,
+                0.38
+            ],
+            "angle": 0,
+            "content": "4.2 3D Position Encoder"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.392,
+                0.784,
+                0.422
+            ],
+            "angle": 0,
+            "content": "After obtaining 3D points by applying PoIs, we first normalize coordinate points using equations as follows:"
+        },
+        {
+            "type": "equation",
+            "bbox": [
+                0.329,
+                0.434,
+                0.786,
+                0.463
+            ],
+            "angle": 0,
+            "content": "\\[\nx, y, z = \\frac {x - x _ {\\operatorname* {m i n}}}{x _ {\\operatorname* {m a x}} - x _ {\\operatorname* {m i n}}}, \\frac {y - y _ {\\operatorname* {m i n}}}{y _ {\\operatorname* {m a x}} - y _ {\\operatorname* {m i n}}}, \\frac {z - z _ {\\operatorname* {m i n}}}{z _ {\\operatorname* {m a x}} - z _ {\\operatorname* {m i n}}}, \\tag {1}\n\\]"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.473,
+                0.798,
+                0.564
+            ],
+            "angle": 0,
+            "content": "where \\([x_{\\min}, y_{\\min}, z_{\\min}, x_{\\max}, y_{\\max}, z_{\\max}] = [-40m, -40m, -1m, 40m, 40m, 5.4m]\\), are the preset boundaries of the scene. Through sine and cosine functions, we encode the normalized coordinates into high-dimensional positional information. Then we use a small MLP containing two linear layers and one ReLU layer to transform the high-dimensional positional information into learnable embeddings."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.216,
+                0.587,
+                0.383,
+                0.601
+            ],
+            "angle": 0,
+            "content": "4.3 Point Decoder"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.611,
+                0.784,
+                0.656
+            ],
+            "angle": 0,
+            "content": "We used three decoder layers to build our point decoder. Each layer of the decoder contains point cross-attention (PCA) and group point cross-attention (GPCA)."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.657,
+                0.788,
+                0.735
+            ],
+            "angle": 0,
+            "content": "The purpose of PCA and GPCA is to integrate position embedding with image features, thereby facilitating a more cohesive representation. Owing to the high efficiency of deformable attention [15], our point decoder mechanisms employ this technique. Consequently, each query within our framework can be updated as follows:"
+        },
+        {
+            "type": "equation",
+            "bbox": [
+                0.36,
+                0.744,
+                0.786,
+                0.786
+            ],
+            "angle": 0,
+            "content": "\\[\n\\mathrm {D A} (\\mathbf {q}, \\mathbf {p}, \\mathbf {F}) = \\sum_ {s = 1} ^ {N _ {s}} \\mathbf {A} _ {s} \\mathbf {W} _ {s} \\mathbf {F} ^ {\\mathbf {2 D}} (\\mathbf {p} + \\Delta \\mathbf {p} _ {s}), \\tag {2}\n\\]"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.795,
+                0.788,
+                0.843
+            ],
+            "angle": 0,
+            "content": "where \\(N_{s}\\) represents the number of sampling offsets, \\(\\mathbf{A}_s\\) represents the learnable attention weights, \\(\\mathbf{F}^{2\\mathbf{D}}(\\mathbf{p} + \\varDelta \\mathbf{p}_s)\\) represents the image features collected at location \\(\\mathbf{p} + \\varDelta \\mathbf{p}_s\\) in which \\(\\varDelta\\mathbf{p}_s\\) represents the offset apply to position \\(\\mathbf{p}\\)."
+        }
+    ],
+    [
+        {
+            "type": "page_number",
+            "bbox": [
+                0.218,
+                0.116,
+                0.23,
+                0.127
+            ],
+            "angle": 0,
+            "content": "8"
+        },
+        {
+            "type": "header",
+            "bbox": [
+                0.272,
+                0.115,
+                0.612,
+                0.13
+            ],
+            "angle": 0,
+            "content": "Y. Shi, T. Cheng, Q. Zhang, W. Liu, and X. Wang"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.147,
+                0.788,
+                0.343
+            ],
+            "angle": 0,
+            "content": "Point cross-attention. The point cross-attention mechanism begins by taking point queries, initially set to zero, and combines them with point position encoding derived from our 3D position encoder. This combination forms the input queries. Subsequently, these queries undergo deformable cross-attention with 2D image features. Given that not all 3D points project onto the image plane, particularly in the context of the six surround views provided by the nuScenes Dataset. Each point is likely to map onto only one or two of these views. We utilize the camera's intrinsic and extrinsic parameters to ascertain which images a given point can map to. This approach ensures that for any specific point, we only consider the features of the image(s) it maps onto, significantly reducing memory consumption. \\(\\mathbf{F}^{2D} = \\{\\mathbf{F}_t^{2D},\\mathbf{F}_{t - 1}^{2D},\\ldots \\}\\) represents the mapped 2D image features where \\(t\\) indexes the images. Therefore, the formula of the point cross-attention can be described as follows:"
+        },
+        {
+            "type": "equation",
+            "bbox": [
+                0.359,
+                0.346,
+                0.786,
+                0.378
+            ],
+            "angle": 0,
+            "content": "\\[\n\\operatorname {P C A} (\\mathbf {q}, \\mathbf {F} ^ {2 D}) = \\frac {1}{| \\mathcal {V} _ {t} |} \\sum_ {t \\in \\mathcal {V} _ {t}} \\mathrm {D A} (\\mathbf {q}, \\mathcal {P} (\\mathbf {p}, t), \\mathbf {F} _ {t} ^ {2 D}), \\tag {3}\n\\]"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.215,
+                0.384,
+                0.787,
+                0.415
+            ],
+            "angle": 0,
+            "content": "where \\(\\mathcal{V}_t\\) represents the hit image, \\(t\\) indexes the images, \\(\\mathcal{P}(\\mathbf{p}, t)\\) represents the projection function of input position \\(\\mathbf{p}\\)."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.215,
+                0.425,
+                0.788,
+                0.53
+            ],
+            "angle": 0,
+            "content": "Group point cross-attention. The group point cross-attention mechanism is aimed at solving the lack of local context in PCA since each point independently interacts with the image features. We adaptively oversample a group of \\( M \\) (\\( M = 8000 \\)) points around our PoIs whose coordinates are calculated by a linear layer. We use the attention obtained from PCA and the 2D image features mapped by our group of points. Therefore, the formula of the group point cross-attention can be described as follows:"
+        },
+        {
+            "type": "equation",
+            "bbox": [
+                0.345,
+                0.533,
+                0.786,
+                0.565
+            ],
+            "angle": 0,
+            "content": "\\[\n\\operatorname {G P C A} \\left(\\mathbf {q} _ {\\mathbf {g}}, \\mathbf {F} ^ {2 D}\\right) = \\frac {1}{\\left| \\mathcal {V} _ {t} \\right|} \\sum_ {t \\in \\mathcal {V} _ {t}} \\mathrm {D A} \\left(\\mathbf {q} _ {\\mathbf {g}}, \\mathcal {P} \\left(\\mathbf {p} _ {\\mathbf {g}}, t\\right), \\mathbf {F} _ {t} ^ {2 D}\\right), \\tag {4}\n\\]"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.215,
+                0.571,
+                0.787,
+                0.601
+            ],
+            "angle": 0,
+            "content": "where \\(\\mathbf{q_g}\\) represents attention calculated by PCA and \\(\\mathbf{p_g}\\) represents the input position of the oversampled group of points."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.216,
+                0.619,
+                0.378,
+                0.633
+            ],
+            "angle": 0,
+            "content": "4.4 Loss Function"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.215,
+                0.641,
+                0.787,
+                0.686
+            ],
+            "angle": 0,
+            "content": "We apply class-wise cross-entropy loss and dice loss to this task. The ground truth \\(\\hat{\\mathbf{G}}_t \\in \\{c_0, c_1, \\dots, c_M\\}\\) represent semantic information of a group of spatial points. The class-wise cross-entropy loss can be computed by:"
+        },
+        {
+            "type": "equation",
+            "bbox": [
+                0.369,
+                0.693,
+                0.786,
+                0.733
+            ],
+            "angle": 0,
+            "content": "\\[\n\\mathcal {L} _ {c e} = - \\sum_ {n = 1} ^ {N} \\sum_ {c = c _ {0}} ^ {c _ {M}} w _ {c} \\hat {g} _ {n, c} \\log \\left(\\frac {e ^ {g _ {n , c}}}{\\sum_ {c} e ^ {g _ {n , c}}}\\right), \\tag {5}\n\\]"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.215,
+                0.74,
+                0.788,
+                0.8
+            ],
+            "angle": 0,
+            "content": "where \\( n \\) is the index of points, \\( N \\) is the number of selected points, \\( c \\) indexes class, \\( g_{n,c} \\) is the predicted logits for the \\( n \\)-th point belonging to class \\( c \\), \\( w_{c} \\) is a weight for each class according to the inverse of the class frequency as in [21]. The dice loss can be computed by:"
+        },
+        {
+            "type": "equation",
+            "bbox": [
+                0.387,
+                0.806,
+                0.786,
+                0.845
+            ],
+            "angle": 0,
+            "content": "\\[\n\\mathcal {L} _ {d i c e} = 1 - \\frac {2 \\sum_ {i = 1} ^ {N} p _ {i} g _ {i}}{\\sum_ {i = 1} ^ {N} p _ {i} ^ {2} + \\sum_ {i = 1} ^ {N} g _ {i} ^ {2}}, \\tag {6}\n\\]"
+        }
+    ],
+    [
+        {
+            "type": "header",
+            "bbox": [
+                0.547,
+                0.115,
+                0.732,
+                0.129
+            ],
+            "angle": 0,
+            "content": "Occupancy as Set of Points"
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.775,
+                0.116,
+                0.787,
+                0.127
+            ],
+            "angle": 0,
+            "content": "9"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.147,
+                0.784,
+                0.178
+            ],
+            "angle": 0,
+            "content": "where \\( p_i \\) is the predicted probability for the point and \\( g_i \\) is the ground truth binary label for the point. Our final loss is the sum of the two losses:"
+        },
+        {
+            "type": "equation",
+            "bbox": [
+                0.436,
+                0.192,
+                0.786,
+                0.207
+            ],
+            "angle": 0,
+            "content": "\\[\n\\mathcal {L} _ {\\text {a l l}} = \\mathcal {L} _ {\\text {d i c e}} + \\mathcal {L} _ {\\text {c e}}. \\tag {7}\n\\]"
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.216,
+                0.232,
+                0.367,
+                0.249
+            ],
+            "angle": 0,
+            "content": "5 Experiment"
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.216,
+                0.266,
+                0.429,
+                0.282
+            ],
+            "angle": 0,
+            "content": "5.1 Experimental Setup"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.294,
+                0.788,
+                0.445
+            ],
+            "angle": 0,
+            "content": "Dataset. We perform our experiments on the nuScenes Dataset with annotation provided by Occ3D. Occ3D-nuScenes benchmark is interested in a volume of \\(80.0\\mathrm{m}\\) to the front and back of the car, \\(80.0\\mathrm{m}\\) to the left and right side, and \\(6.4\\mathrm{m}\\) in height. Each sample is divided as a group of 3D voxel grids with a dimension of [200, 200, 16] since each voxel has a size of \\([0.4m, 0.4m, 0.4m]\\). There are 18 categories of occupancy semantics, of which the 18th category is empty and does not participate in the evaluation. Occ3D-nuScenes provides ground-truth semantical voxel grids by aggregate points of the static scenes and moving objects, respectively. Furthermore, Occ3D-nuScenes utilizes ray-casting-based methods to estimate camera visibility and provides camera visibility masks."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.445,
+                0.787,
+                0.474
+            ],
+            "angle": 0,
+            "content": "Evaluation metric. For this task, we use mIoU as the evaluation metric, which can be formulated as follows:"
+        },
+        {
+            "type": "equation",
+            "bbox": [
+                0.377,
+                0.486,
+                0.786,
+                0.526
+            ],
+            "angle": 0,
+            "content": "\\[\nm I o U = \\frac {1}{C} \\sum_ {c = 1} ^ {C} \\frac {T P _ {c}}{T P _ {c} + F P _ {c} + F N _ {c}}. \\tag {8}\n\\]"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.537,
+                0.786,
+                0.568
+            ],
+            "angle": 0,
+            "content": "Following the definition of 3D occupancy prediction, we only evaluate our results in visible regions."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.568,
+                0.79,
+                0.765
+            ],
+            "angle": 0,
+            "content": "Implementation details. We input six images of the original size into ResNet10 to obtain image features. Our image backbone is pre-trained on the FCOS3D [26], which is the same backbone used in the BEVFormer baseline. Then the features will be taken by FPN to produce multi-scale feature maps. The dimension of features is \\( d = 256 \\). We stacked three decoder layers, with point cross-attention and group point cross-attention in each decoder layer. There is a small MLP as our point-based occupancy predictor that projects the 256 feature dimension to the number of classes. We train our model on 8 NVIDIA 3090 GPUs with 24 epochs. We use the AdamW optimizer with a learning rate of \\( 2 \\times 10^{-4} \\) and a weight decay of 0.01. The learning rate of the backbone is 10 times smaller. Besides, we conducted a separate experiment in which we trained our decoder with a frozen BEVFormer baseline backbone using the same setting above and this decoder will be used as a plugin module for the BEVFormer baseline."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.765,
+                0.807,
+                0.842
+            ],
+            "angle": 0,
+            "content": "Baseline methods. We compare Occupancy as Set of Points with existing methods replicated by Occ3D on Occ3D-nuScenes benchmark, including MonoScene [4], TPVFormer [9], BEVDet [7], OccFormer [32] and BEVFormer [15]. To make a fair comparison with the baseline, we provide results using BEVFormer by our implementation using the camera visible mask during the training."
+        }
+    ],
+    [
+        {
+            "type": "page_number",
+            "bbox": [
+                0.218,
+                0.116,
+                0.236,
+                0.127
+            ],
+            "angle": 0,
+            "content": "10"
+        },
+        {
+            "type": "header",
+            "bbox": [
+                0.272,
+                0.114,
+                0.611,
+                0.13
+            ],
+            "angle": 0,
+            "content": "Y. Shi, T. Cheng, Q. Zhang, W. Liu, and X. Wang"
+        },
+        {
+            "type": "table_caption",
+            "bbox": [
+                0.214,
+                0.145,
+                0.788,
+                0.2
+            ],
+            "angle": 0,
+            "content": "Table 1: 3D Occupancy prediction performance on the Occ3D-nuScenes dataset. * means the performance is achieved by our implementation using the camera mask during training. † means the performance is achieved with a frozen BEVFormer baseline backbone."
+        },
+        {
+            "type": "table",
+            "bbox": [
+                0.223,
+                0.213,
+                0.787,
+                0.382
+            ],
+            "angle": 0,
+            "content": "<table><tr><td>Method</td><td>others</td><td>barrier</td><td>bicycle</td><td>bus</td><td>car</td><td>Cons. Veh</td><td>motorcycle</td><td>pedestrian</td><td>traffic cone</td><td>trailer</td><td>truck</td><td>Dri. Sur</td><td>other flat</td><td>sidewalk</td><td>terrain</td><td>mammade vegetation</td><td>mIoU</td><td></td></tr><tr><td>MonoScene [4]</td><td>1.75</td><td>7.23</td><td>4.26</td><td>4.93</td><td>9.38</td><td>5.67</td><td>3.98</td><td>3.01</td><td>5.90</td><td>4.45</td><td>7.17</td><td>14.91</td><td>6.32</td><td>7.92</td><td>7.43</td><td>1.01</td><td>7.65</td><td>6.06</td></tr><tr><td>TPVFormer [9]</td><td>7.22</td><td>38.90</td><td>13.67</td><td>40.78</td><td>45.90</td><td>17.23</td><td>19.99</td><td>18.85</td><td>14.30</td><td>26.69</td><td>34.17</td><td>55.65</td><td>35.47</td><td>37.55</td><td>30.70</td><td>19.40</td><td>16.78</td><td>27.83</td></tr><tr><td>BEVDet [7]</td><td>4.39</td><td>30.31</td><td>0.23</td><td>32.26</td><td>34.47</td><td>12.97</td><td>10.34</td><td>10.36</td><td>6.26</td><td>8.93</td><td>23.65</td><td>52.27</td><td>24.61</td><td>26.06</td><td>22.31</td><td>15.04</td><td>15.10</td><td>19.38</td></tr><tr><td>OccFormer [32]</td><td>5.94</td><td>30.29</td><td>12.32</td><td>34.40</td><td>39.17</td><td>14.44</td><td>16.45</td><td>17.22</td><td>9.27</td><td>13.90</td><td>26.36</td><td>50.99</td><td>30.96</td><td>34.66</td><td>22.73</td><td>6.76</td><td>6.97</td><td>21.93</td></tr><tr><td>BEVFormer [15]</td><td>5.85</td><td>37.83</td><td>17.87</td><td>40.44</td><td>42.43</td><td>7.36</td><td>23.88</td><td>21.81</td><td>20.98</td><td>22.38</td><td>30.70</td><td>55.35</td><td>28.36</td><td>36.30</td><td>28.06</td><td>20.04</td><td>17.69</td><td>26.88</td></tr><tr><td>CTF-Occ [25]</td><td>8.09</td><td>39.33</td><td>20.56</td><td>38.29</td><td>42.24</td><td>16.93</td><td>24.52</td><td>22.72</td><td>21.05</td><td>22.98</td><td>31.11</td><td>53.33</td><td>33.84</td><td>37.98</td><td>33.23</td><td>20.79</td><td>18.0</td><td>28.53</td></tr><tr><td>SurroundOcc [29]</td><td>8.7</td><td>39.2</td><td>19.7</td><td>41.4</td><td>46.2</td><td>18.7</td><td>20.6</td><td>26.4</td><td>23.3</td><td>27.0</td><td>32.5</td><td>78.0</td><td>38.3</td><td>46.6</td><td>49.6</td><td>36.7</td><td>31.6</td><td>34.4</td></tr><tr><td>OpenOccupancy [27]</td><td>10.4</td><td>45.7</td><td>23.6</td><td>42.4</td><td>49.3</td><td>14.8</td><td>24.6</td><td>27.7</td><td>27.8</td><td>27.6</td><td>33.3</td><td>79.2</td><td>39.8</td><td>47.1</td><td>50.5</td><td>37.7</td><td>31.8</td><td>36.1</td></tr><tr><td>BEVDet-depth [8]</td><td>6.6</td><td>41.2</td><td>7.0</td><td>42.7</td><td>48.4</td><td>18.4</td><td>12.9</td><td>22.0</td><td>18.2</td><td>28.2</td><td>33.2</td><td>80.1</td><td>39.7</td><td>49.1</td><td>52.1</td><td>39.9</td><td>33.8</td><td>33.7</td></tr><tr><td>BEVDet-stereo [8]</td><td>8.6</td><td>45.9</td><td>14.3</td><td>46.0</td><td>51.2</td><td>23.8</td><td>18.9</td><td>24.1</td><td>22.3</td><td>33.6</td><td>37.9</td><td>81.5</td><td>40.5</td><td>52.6</td><td>55.9</td><td>46.9</td><td>41.2</td><td>38.0</td></tr><tr><td>BEVFormer* [15]</td><td>8.54</td><td>46.24</td><td>20.28</td><td>47.46</td><td>53.04</td><td>19.33</td><td>25.39</td><td>26.16</td><td>25.1</td><td>33.45</td><td>37.75</td><td>81.17</td><td>38.64</td><td>49.32</td><td>52.54</td><td>42.73</td><td>36.13</td><td>37.84</td></tr><tr><td>OSP</td><td>11.2</td><td>47.25</td><td>27.06</td><td>47.57</td><td>53.66</td><td>23.21</td><td>29.37</td><td>29.68</td><td>28.41</td><td>32.39</td><td>37.94</td><td>79.35</td><td>41.46</td><td>50.31</td><td>53.23</td><td>40.52</td><td>35.39</td><td>39.41</td></tr><tr><td>\\( OSP^† \\)</td><td>8.87</td><td>46.33</td><td>21.32</td><td>47.51</td><td>53.14</td><td>19.6</td><td>26.12</td><td>26.84</td><td>26.68</td><td>33.67</td><td>37.94</td><td>81.21</td><td>39.13</td><td>49.48</td><td>52.76</td><td>42.73</td><td>36.12</td><td>38.20</td></tr><tr><td>BEVFormer w/ \\( OSP^† \\)</td><td>10.95</td><td>49.0</td><td>27.68</td><td>50.24</td><td>55.99</td><td>22.96</td><td>31.02</td><td>30.91</td><td>30.25</td><td>35.6</td><td>41.23</td><td>82.09</td><td>42.59</td><td>51.9</td><td>55.1</td><td>44.82</td><td>38.17</td><td>41.21</td></tr></table>"
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.216,
+                0.415,
+                0.367,
+                0.429
+            ],
+            "angle": 0,
+            "content": "5.2 Performance"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.445,
+                0.788,
+                0.627
+            ],
+            "angle": 0,
+            "content": "Evaluation strategy. Our evaluation strategy for the method comprehensively utilized the three types of Points of Interest (PoIs) previously outlined. This approach entailed: (1) Standard Grids. We conducted sampling at the centers of grids, enabling us to benchmark our method's mIoU against traditional volume-based methods. This comparison provided a direct assessment of our method's performance in a standard scenario. (2) Adaptively Sampling. We adaptively oversample in our training phase to enhance our performance. Furthermore, we integrated our method as a complementary tool with volume-based approaches. By adaptively selecting PoIs that need refinement, our method enhances and augments existing volume-based techniques as shown in Fig. 3. (3) Manually Sampling. We manually select points out of the standard perception range and test out the model's perceptual ability."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.628,
+                0.787,
+                0.672
+            ],
+            "angle": 0,
+            "content": "Through these varied and thorough evaluation techniques, we are able to comprehensively assess the performance and flexibility of our method across different scenarios and use cases."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.674,
+                0.789,
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+            ],
+            "angle": 0,
+            "content": "Standard grids. In Tab. 1, we benchmarked our method against existing camera-based 3D occupancy prediction techniques on the Occ3D-nuScenes benchmark by setting PoIs to standard grids. Our point-based approach achieved a notable improvement, obtains 1.57 mIoU performance gain compared to our implementation of the Bevformer baseline. Notably, our method outperformed the BEVFormer baseline across almost all category metrics, demonstrating a particularly clear advantage in detecting small targets, such as the bicycle \\((20.28 \\rightarrow 27.06)\\), motorcycle \\((25.39 \\rightarrow 29.37)\\), pedestrian \\((26.16 \\rightarrow 29.37)\\) and traffic cone \\((25.1 \\rightarrow 28.31)\\). This advantage is mainly because the direct sampling of spatial points is beneficial for feature extraction and mapping of small objects."
+        }
+    ],
+    [
+        {
+            "type": "header",
+            "bbox": [
+                0.548,
+                0.115,
+                0.732,
+                0.129
+            ],
+            "angle": 0,
+            "content": "Occupancy as Set of Points"
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.769,
+                0.116,
+                0.784,
+                0.127
+            ],
+            "angle": 0,
+            "content": "11"
+        },
+        {
+            "type": "image",
+            "bbox": [
+                0.218,
+                0.147,
+                0.78,
+                0.337
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "image_caption",
+            "bbox": [
+                0.214,
+                0.346,
+                0.788,
+                0.417
+            ],
+            "angle": 0,
+            "content": "Fig. 3: Pipeline of refining volume-based methods with OSP. Given RGB images, 2D features are extracted by the frozen image backbone of the volume-based method which in our case is the BEVFormer baseline. We use the volume-based decoder to infer the entire scene, the point decoder to infer our selected 3D points, and combine the results of both using a weighted sum method."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.508,
+                0.788,
+                0.691
+            ],
+            "angle": 0,
+            "content": "Adaptively Sampling. (1) The results shown in Tab. 4 demonstrate that our adaptively oversampling strategy improved the results from 38.01 to 38.48. (2) In the adaptive refinement experiment, we use our own implementation of the BEVFormer baseline as a representative of volume-based methods and enhance it with our method using different scales of PoIs by adaptively selecting locations with low confidence. Our model is trained with the frozen BEVFormer baseline backbone. The experimental results, as shown in Tab. 3, confirm that our plugin model significantly enhances BEVformer's performance. Notably, the BEVFormer w/ OSP results surpass those obtained from the two models operating independently. Furthermore, as we increase the size of the refined scene, the mIoU scores of BEVFormer w/ OSP also exhibit marked improvements, indicating that our refinement approach is very effective across various areas."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.72,
+                0.788,
+                0.841
+            ],
+            "angle": 0,
+            "content": "Manually Sampling. The Occ3D-nuScenes dataset offers annotations across an \\([80m\\times 80m]\\) range. We manually set the points and annotations within a smaller \\([60m\\times 60m]\\) area and set PoIs to points out of it. To comprehensively evaluate our method, we conducted assessments across three distinct ranges: the standard \\([60m\\times 60m]\\) range, the full dataset range of \\([80m\\times 80m]\\), and an intermediate-range spanning from \\(60m\\) to \\(80m\\). Results shown in Tab. 2 demonstrate the capability of our method to make predictions at manually selected locations which is outside the predefined range in this case."
+        }
+    ],
+    [
+        {
+            "type": "page_number",
+            "bbox": [
+                0.218,
+                0.116,
+                0.236,
+                0.127
+            ],
+            "angle": 0,
+            "content": "12"
+        },
+        {
+            "type": "header",
+            "bbox": [
+                0.272,
+                0.115,
+                0.612,
+                0.13
+            ],
+            "angle": 0,
+            "content": "Y. Shi, T. Cheng, Q. Zhang, W. Liu, and X. Wang"
+        },
+        {
+            "type": "table_caption",
+            "bbox": [
+                0.216,
+                0.145,
+                0.788,
+                0.187
+            ],
+            "angle": 0,
+            "content": "Table 2: Predict regions beyond the scene. The model was trained on scenes within a \\(60m \\times 60m\\) range, and evaluations were conducted on the training scenes, the entire size of the scene, and the distance from \\(60m\\) to \\(80m\\)."
+        },
+        {
+            "type": "table",
+            "bbox": [
+                0.229,
+                0.199,
+                0.772,
+                0.273
+            ],
+            "angle": 0,
+            "content": "<table><tr><td>Method</td><td>Scene</td><td>Range</td><td>mIoU</td></tr><tr><td>OSP</td><td>Standard</td><td>0m ~ 60m</td><td>42.29</td></tr><tr><td>OSP</td><td>Large</td><td>0m ~ 80m</td><td>29.00</td></tr><tr><td>OSP</td><td>Beyond</td><td>60m ~ 80m</td><td>23.00</td></tr></table>"
+        },
+        {
+            "type": "table_caption",
+            "bbox": [
+                0.216,
+                0.302,
+                0.787,
+                0.37
+            ],
+            "angle": 0,
+            "content": "Table 3: Refine BEVFormer with our decoder. \\(\\dagger\\) means our model is trained with a frozen BEVFormer baseline backbone. Refine scale refers to an area defined by the length multiplied by the width, centered around the self-vehicle. The entire scene spans a scale of \\(80m\\times 80m\\). As the refine scale increases, the mIoU also gradually increases."
+        },
+        {
+            "type": "table",
+            "bbox": [
+                0.231,
+                0.384,
+                0.773,
+                0.505
+            ],
+            "angle": 0,
+            "content": "<table><tr><td>Method</td><td>Refine Scale</td><td>mIoU</td></tr><tr><td>BEVFormer</td><td>-</td><td>37.84</td></tr><tr><td>\\( OSP^† \\)</td><td>-</td><td>38.20</td></tr><tr><td>BEVFormer w/ OSP</td><td>20m × 20m</td><td>38.20</td></tr><tr><td>BEVFormer w/ OSP</td><td>40m × 40m</td><td>39.35</td></tr><tr><td>BEVFormer w/ OSP</td><td>50m × 50m</td><td>40.32</td></tr><tr><td>BEVFormer w/ OSP</td><td>80m × 80m</td><td>41.21</td></tr></table>"
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.216,
+                0.541,
+                0.398,
+                0.555
+            ],
+            "angle": 0,
+            "content": "5.3 Ablation studies"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.574,
+                0.788,
+                0.696
+            ],
+            "angle": 0,
+            "content": "Model architecture. We conducted ablation experiments on the model structure, particularly exploring variations in the number of transformer layers and the adaptively oversampling strategy. The results of these experiments are presented in Tab. 4. Even without an oversampling strategy and with just a 3-layer transformer, our method's baseline already surpasses the BEVFormer baseline, advancing from 37.84 to 38.01 in mIoU. The oversampling strategy further amplifies this improvement by enhancing the connections between spatial points, culminating in an increased mIoU of 38.48."
+        },
+        {
+            "type": "table_caption",
+            "bbox": [
+                0.216,
+                0.725,
+                0.787,
+                0.753
+            ],
+            "angle": 0,
+            "content": "Table 4: Model architecture. To make a fair comparison with the baseline, we set the number of layers to three. The oversampling strategy brings \\(0.47\\mathrm{mIoU}\\)."
+        },
+        {
+            "type": "table",
+            "bbox": [
+                0.229,
+                0.765,
+                0.772,
+                0.838
+            ],
+            "angle": 0,
+            "content": "<table><tr><td>Layer Num</td><td>Oversample</td><td>mIoU</td></tr><tr><td>1</td><td>-</td><td>36.76</td></tr><tr><td>3</td><td>-</td><td>38.01</td></tr><tr><td>3</td><td>✓</td><td>38.48</td></tr></table>"
+        }
+    ],
+    [
+        {
+            "type": "header",
+            "bbox": [
+                0.548,
+                0.115,
+                0.732,
+                0.129
+            ],
+            "angle": 0,
+            "content": "Occupancy as Set of Points"
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.769,
+                0.116,
+                0.786,
+                0.127
+            ],
+            "angle": 0,
+            "content": "13"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.147,
+                0.788,
+                0.237
+            ],
+            "angle": 0,
+            "content": "2D image features. The performance of our method is heavily reliant on the quality of image features. To elucidate this dependency, we conducted ablation studies focusing on the output from the model's 'neck' component. These experiments demonstrated the substantial impact of using a multi-scale output from the FPN. This architectural choice boosts the model's performance, as evidenced by the increase in mIoU from 38.67 to 39.41 as shown in Tab. 5."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.238,
+                0.788,
+                0.314
+            ],
+            "angle": 0,
+            "content": "Grid center sampling method. Our ablation experiments also investigated the technique for the grid center sampling method during training. While initially using grid center points as coordinates, we introduced a variation by adding random perturbations. The results, as detailed in Tab. 5, indicate the introduction of this disturbance to the grid center points elevates the mIoU to 38.67."
+        },
+        {
+            "type": "table_caption",
+            "bbox": [
+                0.214,
+                0.338,
+                0.788,
+                0.394
+            ],
+            "angle": 0,
+            "content": "Table 5: 2D image features and point perturbation. Increasing the multi-scale feature maps of images from 2 to 4 brings \\(0.74\\mathrm{mIoU}\\). We randomly applied perturbations of \\(0.1m\\) to the coordinates of each point in three directions. This strategy brings \\(0.19\\mathrm{mIoU}\\)"
+        },
+        {
+            "type": "table",
+            "bbox": [
+                0.229,
+                0.407,
+                0.772,
+                0.495
+            ],
+            "angle": 0,
+            "content": "<table><tr><td>Multi-scale Images Features</td><td>Point Perturbation</td><td>mIoU</td></tr><tr><td>2</td><td>-</td><td>38.48</td></tr><tr><td>2</td><td>✓</td><td>38.67</td></tr><tr><td>4</td><td>✓</td><td>39.41</td></tr></table>"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.521,
+                0.788,
+                0.611
+            ],
+            "angle": 0,
+            "content": "Loss design. The task of 3D occupancy prediction bears similarities to segmentation tasks. In the context of the Occ3D-nuScenes dataset, there is a notable issue of class imbalance. To address this challenge, we opted to incorporate dice loss into our optimization strategy. The effectiveness of this choice is evident in the results presented in Tab. 6, which demonstrate that the integration of dice loss benefits our method."
+        },
+        {
+            "type": "table_caption",
+            "bbox": [
+                0.226,
+                0.634,
+                0.776,
+                0.649
+            ],
+            "angle": 0,
+            "content": "Table 6: Loss design. Incorporating dice loss for optimization brings \\(0.53\\mathrm{mIoU}\\)."
+        },
+        {
+            "type": "table",
+            "bbox": [
+                0.229,
+                0.661,
+                0.772,
+                0.721
+            ],
+            "angle": 0,
+            "content": "<table><tr><td>Dice Loss</td><td>mIoU</td></tr><tr><td>-</td><td>38.88</td></tr><tr><td>✓</td><td>39.41</td></tr></table>"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.75,
+                0.788,
+                0.841
+            ],
+            "angle": 0,
+            "content": "Adaptive point sampling. During the inference process, we apply adaptive sampling (adaptively select points with high uncertainty) in BEVFormer w/ OSP and OSP. Adaptive sampling can reduce computational burden while maintaining good performance as shown in Tab. 7. BEVFormer w/ OSP using adaptive sampling means we refine BEVFormer by adaptively selecting points with high uncertainty (around \\(20\\%\\)). OSP using adaptive sampling means we only forward"
+        }
+    ],
+    [
+        {
+            "type": "page_number",
+            "bbox": [
+                0.218,
+                0.116,
+                0.236,
+                0.127
+            ],
+            "angle": 0,
+            "content": "14"
+        },
+        {
+            "type": "header",
+            "bbox": [
+                0.271,
+                0.114,
+                0.612,
+                0.13
+            ],
+            "angle": 0,
+            "content": "Y. Shi, T. Cheng, Q. Zhang, W. Liu, and X. Wang"
+        },
+        {
+            "type": "table_caption",
+            "bbox": [
+                0.315,
+                0.145,
+                0.687,
+                0.159
+            ],
+            "angle": 0,
+            "content": "Table 7: Adaptability of OSP. 'rel' denotes relative."
+        },
+        {
+            "type": "table",
+            "bbox": [
+                0.229,
+                0.172,
+                0.77,
+                0.279
+            ],
+            "angle": 0,
+            "content": "<table><tr><td>Method</td><td>Adaptive Sampling</td><td>Rel. Computation</td><td>mIoU</td></tr><tr><td>BEVFormer</td><td>-</td><td>-</td><td>37.84</td></tr><tr><td>BEVFormer w/ OSP</td><td>-</td><td>1.0×</td><td>39.35</td></tr><tr><td>BEVFormer w/ OSP</td><td>✓</td><td>0.8×</td><td>39.22</td></tr><tr><td>OSP</td><td>-</td><td>1.0×</td><td>39.41</td></tr><tr><td>OSP</td><td>✓</td><td>0.93×</td><td>39.08</td></tr></table>"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.309,
+                0.785,
+                0.369
+            ],
+            "angle": 0,
+            "content": "points with low confidence to the next decoder layer, while directly outputting the results for points with high confidence in the decoder. Points of high uncertainty are defined as those with a confidence score less than a threshold of 0.9 after softmax."
+        },
+        {
+            "type": "image",
+            "bbox": [
+                0.225,
+                0.398,
+                0.784,
+                0.563
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "image_caption",
+            "bbox": [
+                0.214,
+                0.575,
+                0.788,
+                0.618
+            ],
+            "angle": 0,
+            "content": "Fig. 4: Visualization of our results. Our visualization results have voids compared to the ground truth ego-vehicle position, which is due to the surrounding view having gaps near the ego-vehicle."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.216,
+                0.681,
+                0.36,
+                0.697
+            ],
+            "angle": 0,
+            "content": "6 Conclusion"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.214,
+                0.72,
+                0.788,
+                0.841
+            ],
+            "angle": 0,
+            "content": "In this work, we present a novel perspective on 3D scene representation, viewing it through a set of points. We introduce the innovative concept of Points of Interest (PoIs), which significantly advances the flexibility in scene representation. Building upon the foundation laid by PoIs, we develop a highly adaptable, point-based 3D occupancy prediction framework, named OSP. We validate the strong performance and flexibility of OSP on the Occ3D-nuScenes benchmark. Our work not only contributes to the field of 3D occupancy prediction but also paves the way for more dynamic and adaptable methods in 3D scene analysis."
+        }
+    ],
+    [
+        {
+            "type": "header",
+            "bbox": [
+                0.548,
+                0.115,
+                0.732,
+                0.129
+            ],
+            "angle": 0,
+            "content": "Occupancy as Set of Points"
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.769,
+                0.117,
+                0.785,
+                0.127
+            ],
+            "angle": 0,
+            "content": "15"
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.218,
+                0.145,
+                0.403,
+                0.163
+            ],
+            "angle": 0,
+            "content": "Acknowledgements"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.217,
+                0.179,
+                0.785,
+                0.224
+            ],
+            "angle": 0,
+            "content": "This work was supported by National Natural Science Foundation of China (No. 62376102) and the Agricultural Science and Technology Independent Innovation Foundation of Jiangsu Province, China, No. CX(22)2031."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.218,
+                0.249,
+                0.323,
+                0.265
+            ],
+            "angle": 0,
+            "content": "References"
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2024/Occupancy as Set of Points/e88bbcd3-f64f-4fcb-a277-94c463373078_origin.pdf

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+# Occupancy as Set of Points
+
+Yiang Shi $^{1,\star}$ , Tianheng Cheng $^{1,\star}$ , Qian Zhang $^{2}$ , Wenyu Liu $^{1}$ , and Xinggang Wang $^{1,\text{忍}}$
+
+$^{1}$  School of EIC, Huazhong University of Science & Technology
+
+2 Horizon Robotics
+
+Abstract. In this paper, we explore a novel point representation for 3D occupancy prediction from multi-view images, which is named Occupancy as Set of Points. Existing camera-based methods tend to exploit dense volume-based representation to predict the occupancy of the whole scene, making it hard to focus on the special areas or areas out of the perception range. In comparison, we present the Points of Interest (PoIs) to represent the scene and propose OSP, a novel framework for point-based 3D occupancy prediction. Owing to the inherent flexibility of the point-based representation, OSP achieves strong performance compared with existing methods and excels in terms of training and inference adaptability. It extends beyond traditional perception boundaries and can be seamlessly integrated with volume-based methods to significantly enhance their effectiveness. Experiments on the Occ3D-nuScenes occupancy benchmark show that OSP has strong performance and flexibility. Code and models are available at https://github.com/hustvl/osp.
+
+Keywords: 3D Occupancy Prediction  $\cdot$  Autonomous Vehicles  $\cdot$  Multi-view 3D Perception
+
+# 1 Introduction
+
+Holistic 3D scene understanding is crucial for autonomous driving systems, directly affecting the efficiency and accuracy of subsequent tasks. Considering the cost-effectiveness and ease of deployment of cameras compared to other sensors, developing visual-based methods for 3D scene understanding has become a significant and widely researched challenge.
+
+To tackle this challenge, 3D Semantic Scene Completion (SSC) [22] has been proposed and widely studied to jointly infer the geometry and semantics information of the scene from limited observations. The SSC task requires the model to accurately predict the visible locations and complete the information for the invisible locations. Recently, Occ3D [25] introduces a new task definition called 3D occupancy prediction. The main difference between this task and SSC is that 3D occupancy prediction only focuses on the visible areas and is tailored for dynamic scenes.
+
+![](images/c2db89918190d37d13c18068154ca7442a3a75eef145310947f2ffd263686312.jpg)  
+Fig. 1: Comparison between volume-based methods and our method. The volume-based methods, represented by BEVFormer, infers every region within the scene and gets standard occupancy as shown in (a). Our method uses a point-based decoder as shown in (b). Thus it infers the Points of Interest including standard, adaptively sampled, and manually sampled grids as shown in (c).
+
+![](images/cecde533082c1cb6a3ea0177789a83d58650a9cc44368b218888afd5ac117637.jpg)
+
+Existing 3D occupancy prediction methods are mostly based on dense BEV methods, e.g., BEVFormer [15], BEVDet [7]. These methods integrate a BEV encoder with an occupancy head to generate the output and enhance BEV perception capabilities for better results. However, they share some common drawbacks. (1) Uniform Sampling: BEV-based methods fail to differentiate between different areas within the same scene, treating them equally. This leads to coarse sampling and hinders dynamic or multi-resolution sampling capabilities. (2) Limited Inference Flexibility: During inference, these methods can only process the entire scene at once. They lack the ability to infer different parts of the scene based on varying downstream tasks or specific practical needs.
+
+Those limitations highlight the need for more flexible 3D occupancy prediction methods that can handle complex scenes while adapting to different inference requirements. In this paper, we propose a novel point-based representation for 3D occupancy prediction. Instead of dividing the scene into uniform grids applied by existing volume-based methods, we propose Points of Interest (PoIs) to view the scene as a collection of points that help in flexibly sampling the scene during both training and inference stages. Fig. 1 compares volume-based and point-based representations. Compared to volume-based representations, our point-based representation has the following advantages: (1) it can accept inputs of any scale and position to make occupancy predictions, including manually designed and adaptively designed input, offering flexibility; (2) it can pay extra attention to certain areas rather than treating all areas equally, enhancing the model's perceptual capabilities.
+
+We introduce Occupancy as Set of Points (OSP), a novel and flexible point-based framework, which is built upon the foundational concept of Points of Interest (PoIs). OSP excels in 3D occupancy prediction and is composed of an image backbone, a 3D positioning encoder, and a decoder, as illustrated in Fig. 2. Central to our methodology is the innovative use of PoIs, which we have categorized into three distinct types to meet diverse needs, thereby significantly enhancing various aspects of our model's performance. Detailed descriptions of these PoIs will be provided in Sec. 3. Notably, PoIs can be designed as needed beyond the three types initially proposed by us.
+
+Our method stands out for its strong performance and flexibility. The flexibility enables it to process any arbitrary local scene without necessitating retraining. Additionally, our method can serve as an augmentative plugin module for existing volume-based methods by adaptively resampling areas of low confidence to yield more accurate occupancy predictions. It is also adept at predicting areas beyond the scene. The key contributions of our approach can be summarized as follows:
+
+- A novel point-based occupancy representation, established by interacting point queries with 2D image features, enables a comprehensive understanding of 3D scenes.  
+- A flexible framework that allows for inference at any area of interest without retraining or sacrificing accuracy and predicts areas beyond the scene.  
+- A plugin module that enhances the performance of the volume-based baseline significantly.  
+- OSP has obtained strong experiment results, i.e., 39.4 mIoU in the 3D occupancy prediction task on the Occ3D-nuScenes benchmark.
+
+# 2 Related Work
+
+# 2.1 3D Occupancy Prediction
+
+3D occupancy prediction, a concept recently defined by Occ3D [25], exhibits notable parallels with Occupancy Grid Mapping (OGM) [20, 24, 27] used in robotics. This task aims to predict the state of each voxel grid in a scene based on a series of sensor inputs. Occ3D establishes two benchmarks leveraging the Waymo Open Dataset [23] and the nuScenes Dataset [2] to facilitate this. In vision-based 3D Occupancy prediction, Occ3D implements camera visibility estimation and creates visibility masks to ensure evaluations are confined to visible areas. It also evaluates various SSC methodologies on its benchmarks, including MonoScene [4], TPVFormer [9], BEVDet [7], OccFormer [32], and BEV-Former [15].
+
+# 2.2 3D Semantic Scene Completion
+
+Scene Semantic Completion (SSC) represents a task closely associated with 3D occupancy prediction. The concept of SSC is initially presented in SSC-Net [22], with a focus on predicting the comprehensive semantic information
+
+of a scene based on its partially visible regions. Over recent years, the study of SSC has expanded significantly, particularly in the context of small indoor scenes [3,5,11-13,16,30,31]. In recent times, the study of Scene Semantic Completion (SSC) for expansive outdoor environments has gained momentum, particularly following the introduction of the SemanticKITTI dataset [1]. Notably, MonoScene [4] emerges as the first method to apply monocular pure vision-based SSC. In a parallel advancement, OccDepth [19] enhances 2D to 3D feature transformation by incorporating depth data from stereo input. TPVFormer [9] argues against the limitations of single-plane modeling in capturing intricate details, hence it adopts a tri-perspective view (TPV) approach, combining a Bird's Eye View (BEV) with two additional vertical planes. Additionally, Symphonies [10] highlights the significance of instance representation in SSC tasks. While SSC methods can be directly applied to 3D occupancy prediction, two primary distinctions exist: (1) SSC primarily aims to infer the occupancy of non-visible areas based on visible regions, in contrast to 3D occupancy prediction which focuses on visible areas; (2) SSC methods usually target static scenes, whereas 3D occupancy prediction methods are often designed to handle dynamic scenes.
+
+Most existing volume-based SSC and 3D occupancy prediction methods are characterized by their dense nature, encompassing inputs and outputs that span the entire scene. Consider the BEVFormer baseline as an example: it segments the scene into uniform BEV grids, failing to distinguish between grids in varying areas. This uniformity restricts the ability of volume-based methods like BEVFormer to sample areas of interest for better performance during training. Besides, if we want to focus on a specific area in the inference stage, volume-based methods are limited to infer the entire scene and then perform post-processing, inevitably leading to increased and unnecessary costs. Moreover, as scene size and voxel resolution increase, the computational demands skyrocket exponentially. In stark contrast, our point-based model introduces much-needed flexibility by focusing on PoIs. Our method facilitates direct inference in specific areas, eliminating the need for post-processing and avoiding additional computational burdens.
+
+A point-related SSC method is PointOcc [34], a point cloud-based SSC prediction method using three complementary view planes for efficient point cloud feature modeling and an efficient 2D backbone for processing to reduce computational load, while our method focuses on the flexibility of training and inference.
+
+# 2.3 Camera-based 3D Detection
+
+Camera-based 3D perception tasks have gained substantial attention in recent research, largely due to the convenience and cost-effectiveness of cameras as data collection sensors. Initial efforts, such as FCOS3D [26] and DETR3D [28], explore the transition from 2D to 3D predictions. BEVFormer [15] represents a significant advancement in this area, transforming images captured from vehicle-mounted cameras into a bird's eye view (BEV) representation. This technique not only enhances a vehicle's environmental understanding but also finds appli
+
+cations in various downstream tasks like BEVStereo [14] and BEVDet [7] and extends to 3D occupancy prediction.
+
+3D detection tasks are important in camera-based 3D perception and also have a high similarity to 3D occupancy prediction. The primary objective of 3D detection involves estimating the position and dimensions of objects in 3D space. DETR3D [28], drawing inspiration from DETR [33], innovatively combines 3D object queries with image features, incorporating camera intrinsic and extrinsic parameters. PETR [17] and its successor, PETRv2 [18], further refine this approach by addressing the accuracy of reference point sampling and the inclusion of global information, enhancing 3D detection through historical data integration. Compared to object detection, 3D occupancy prediction offers a finer granularity, which is crucial for navigating irregular obstacles or overhanging objects. It is imperative to explore how insights from BEV representation and 3D object detection can inspire innovative solutions tailored to the specific requirements of 3D occupancy prediction.
+
+# 3 Preliminary
+
+Problem setup. We aim to provide occupancy predictions around the ego-vehicle, given only  $N$  surround-view RGB images. More specifically, we use as input current images denoted by  $\mathbf{I}_i = \{I_0,I_1,\dots,I_N\}$ , and use as output an occupancy prediction  $\mathbf{Y}_i\in \{c_0,c_1,\dots,c_M\}^{H\times W\times Z}$  defined in the coordinate of ego-vehicle, where each occupancy prediction is either empty (denoted by  $c_{0}$ ) or occupied by a certain semantic class in  $\{c_{1},c_{m},\dots,c_{M}\}$ . We assume known camera intrinsic parameters  $\{K_i\}$  and extrinsic parameters  $\{[R_i|t_i]\}$  in each frame. We assume to know whether each area is visible or not by applying a camera visibility mask.
+
+Points of Interest. In our model, we innovatively introduce the concept of Points of Interest (PoIs), which is a set of sparse points to represent the 3D scene. PoIs can flexibly represent objects or regions that require extra attention such as pedestrians or regions near ego-vehicles and can be designed as needed in both training and inference phases. We use three types of PoIs and the definitions and functions are introduced as follows:
+
+(1) Standard Grids: By sampling center points of grids in the inference stage and making predictions of standard 3D occupancy grids, our model makes a fair comparison with existing methods and achieves good results.  
+(2) Adaptively Sampling: During the training stage, our model adaptively samples points and oversamples points around them to enhance accuracy. Recognizing that volume-based methods uniformly treat all locations, our point-based approach allows for resampling in either areas of special interest or those that are challenging to learn. This adaptively resampling strategy is also used to augment the performance of volume-based methods. Consequently, our method can function as a versatile plugin, seamlessly integrating with and enhancing existing volume-based approaches.
+
+(3) Manually Sampling: Our model excels in its flexibility to sample any area, specifically catering to the unique demands of various downstream tasks. Our model can make predictions of areas beyond the standard perception range by setting PoIs to areas outside the scene manually, e.g. 200 meters away from ego-vehicle, which is a feat unattainable by traditional volume-based methods. This extension not only broadens the scope of inference but also introduces a new dimension to scene understanding. These PoIs are the foundation of our method, offering precision and high flexibility.
+
+![](images/449a3e8aed3cf8a8fcbc855e54bd5977137f27256f6b3f0d14dcfd7ea7ae6ea4.jpg)  
+Fig. 2: Overall framework of Occupancy as Set of Points. OSP leverages the Transformer architecture to derive 3D point features from 2D images to make 3D occupancy predictions. Initially, we extract 2D features from multi-view images. Following this, we employ a set of 3D point queries to index these 2D features. The selection of these 3D point queries depends on the Points of Interest (PoIs).
+
+# 4 Method
+
+# 4.1 Overall Architecture
+
+Fig. 2 shows the overall architecture of the proposed Occupancy as Set of Points. Given the images  $\mathbf{I} = \{I_i, i = 1, 2, \dots, N\}$  from  $\mathbf{N}$  views, We feed these images into ResNet [6] to obtain their features.
+
+OSP begins with sampling a set of 3D points in the space, which are the initial PoIs. In our experiments, we use center points of grids as the initial PoIs to make a fair comparison with traditional volume-based methods and provide a reliable baseline for performance comparison and evaluation. We sample  $K(K = 8000)$
+
+points within the camera's visible region and introduce random perturbations to these points.
+
+Then we normalize the 3D points. To these normalized coordinates, we apply sine and cosine functions as a form of positional encoding. This encoded positional information is then utilized to create query position embeddings. In the training phase, within each decoder layer, the query position corresponding to each individual query remains consistent. The 3D coordinates, along with the camera's intrinsic and extrinsic parameters, are used to map these points onto the pixel plane. This mapping process yields corresponding key and value pairs. Subsequently, we employ point cross-attention mechanisms to compute the output. Then we adaptively oversample a group of  $M$  points ( $M = 8000$ ) whose coordinates are calculated by a linear layer and employ group point cross-attention to fuse the features of the additional sampling points.
+
+# 4.2 3D Position Encoder
+
+After obtaining 3D points by applying PoIs, we first normalize coordinate points using equations as follows:
+
+$$
+x, y, z = \frac {x - x _ {\operatorname* {m i n}}}{x _ {\operatorname* {m a x}} - x _ {\operatorname* {m i n}}}, \frac {y - y _ {\operatorname* {m i n}}}{y _ {\operatorname* {m a x}} - y _ {\operatorname* {m i n}}}, \frac {z - z _ {\operatorname* {m i n}}}{z _ {\operatorname* {m a x}} - z _ {\operatorname* {m i n}}}, \tag {1}
+$$
+
+where  $[x_{\min}, y_{\min}, z_{\min}, x_{\max}, y_{\max}, z_{\max}] = [-40m, -40m, -1m, 40m, 40m, 5.4m]$ , are the preset boundaries of the scene. Through sine and cosine functions, we encode the normalized coordinates into high-dimensional positional information. Then we use a small MLP containing two linear layers and one ReLU layer to transform the high-dimensional positional information into learnable embeddings.
+
+# 4.3 Point Decoder
+
+We used three decoder layers to build our point decoder. Each layer of the decoder contains point cross-attention (PCA) and group point cross-attention (GPCA).
+
+The purpose of PCA and GPCA is to integrate position embedding with image features, thereby facilitating a more cohesive representation. Owing to the high efficiency of deformable attention [15], our point decoder mechanisms employ this technique. Consequently, each query within our framework can be updated as follows:
+
+$$
+\mathrm {D A} (\mathbf {q}, \mathbf {p}, \mathbf {F}) = \sum_ {s = 1} ^ {N _ {s}} \mathbf {A} _ {s} \mathbf {W} _ {s} \mathbf {F} ^ {\mathbf {2 D}} (\mathbf {p} + \Delta \mathbf {p} _ {s}), \tag {2}
+$$
+
+where  $N_{s}$  represents the number of sampling offsets,  $\mathbf{A}_s$  represents the learnable attention weights,  $\mathbf{F}^{2\mathbf{D}}(\mathbf{p} + \varDelta \mathbf{p}_s)$  represents the image features collected at location  $\mathbf{p} + \varDelta \mathbf{p}_s$  in which  $\varDelta\mathbf{p}_s$  represents the offset apply to position  $\mathbf{p}$ .
+
+Point cross-attention. The point cross-attention mechanism begins by taking point queries, initially set to zero, and combines them with point position encoding derived from our 3D position encoder. This combination forms the input queries. Subsequently, these queries undergo deformable cross-attention with 2D image features. Given that not all 3D points project onto the image plane, particularly in the context of the six surround views provided by the nuScenes Dataset. Each point is likely to map onto only one or two of these views. We utilize the camera's intrinsic and extrinsic parameters to ascertain which images a given point can map to. This approach ensures that for any specific point, we only consider the features of the image(s) it maps onto, significantly reducing memory consumption.  $\mathbf{F}^{2D} = \{\mathbf{F}_t^{2D},\mathbf{F}_{t - 1}^{2D},\ldots \}$  represents the mapped 2D image features where  $t$  indexes the images. Therefore, the formula of the point cross-attention can be described as follows:
+
+$$
+\operatorname {P C A} (\mathbf {q}, \mathbf {F} ^ {2 D}) = \frac {1}{| \mathcal {V} _ {t} |} \sum_ {t \in \mathcal {V} _ {t}} \mathrm {D A} (\mathbf {q}, \mathcal {P} (\mathbf {p}, t), \mathbf {F} _ {t} ^ {2 D}), \tag {3}
+$$
+
+where  $\mathcal{V}_t$  represents the hit image,  $t$  indexes the images,  $\mathcal{P}(\mathbf{p}, t)$  represents the projection function of input position  $\mathbf{p}$ .
+
+Group point cross-attention. The group point cross-attention mechanism is aimed at solving the lack of local context in PCA since each point independently interacts with the image features. We adaptively oversample a group of  $M$  ( $M = 8000$ ) points around our PoIs whose coordinates are calculated by a linear layer. We use the attention obtained from PCA and the 2D image features mapped by our group of points. Therefore, the formula of the group point cross-attention can be described as follows:
+
+$$
+\operatorname {G P C A} \left(\mathbf {q} _ {\mathbf {g}}, \mathbf {F} ^ {2 D}\right) = \frac {1}{\left| \mathcal {V} _ {t} \right|} \sum_ {t \in \mathcal {V} _ {t}} \mathrm {D A} \left(\mathbf {q} _ {\mathbf {g}}, \mathcal {P} \left(\mathbf {p} _ {\mathbf {g}}, t\right), \mathbf {F} _ {t} ^ {2 D}\right), \tag {4}
+$$
+
+where  $\mathbf{q_g}$  represents attention calculated by PCA and  $\mathbf{p_g}$  represents the input position of the oversampled group of points.
+
+# 4.4 Loss Function
+
+We apply class-wise cross-entropy loss and dice loss to this task. The ground truth  $\hat{\mathbf{G}}_t \in \{c_0, c_1, \dots, c_M\}$  represent semantic information of a group of spatial points. The class-wise cross-entropy loss can be computed by:
+
+$$
+\mathcal {L} _ {c e} = - \sum_ {n = 1} ^ {N} \sum_ {c = c _ {0}} ^ {c _ {M}} w _ {c} \hat {g} _ {n, c} \log \left(\frac {e ^ {g _ {n , c}}}{\sum_ {c} e ^ {g _ {n , c}}}\right), \tag {5}
+$$
+
+where  $n$  is the index of points,  $N$  is the number of selected points,  $c$  indexes class,  $g_{n,c}$  is the predicted logits for the  $n$ -th point belonging to class  $c$ ,  $w_{c}$  is a weight for each class according to the inverse of the class frequency as in [21]. The dice loss can be computed by:
+
+$$
+\mathcal {L} _ {d i c e} = 1 - \frac {2 \sum_ {i = 1} ^ {N} p _ {i} g _ {i}}{\sum_ {i = 1} ^ {N} p _ {i} ^ {2} + \sum_ {i = 1} ^ {N} g _ {i} ^ {2}}, \tag {6}
+$$
+
+where  $p_i$  is the predicted probability for the point and  $g_i$  is the ground truth binary label for the point. Our final loss is the sum of the two losses:
+
+$$
+\mathcal {L} _ {\text {a l l}} = \mathcal {L} _ {\text {d i c e}} + \mathcal {L} _ {\text {c e}}. \tag {7}
+$$
+
+# 5 Experiment
+
+# 5.1 Experimental Setup
+
+Dataset. We perform our experiments on the nuScenes Dataset with annotation provided by Occ3D. Occ3D-nuScenes benchmark is interested in a volume of  $80.0\mathrm{m}$  to the front and back of the car,  $80.0\mathrm{m}$  to the left and right side, and  $6.4\mathrm{m}$  in height. Each sample is divided as a group of 3D voxel grids with a dimension of [200, 200, 16] since each voxel has a size of  $[0.4m, 0.4m, 0.4m]$ . There are 18 categories of occupancy semantics, of which the 18th category is empty and does not participate in the evaluation. Occ3D-nuScenes provides ground-truth semantical voxel grids by aggregate points of the static scenes and moving objects, respectively. Furthermore, Occ3D-nuScenes utilizes ray-casting-based methods to estimate camera visibility and provides camera visibility masks.
+
+Evaluation metric. For this task, we use mIoU as the evaluation metric, which can be formulated as follows:
+
+$$
+m I o U = \frac {1}{C} \sum_ {c = 1} ^ {C} \frac {T P _ {c}}{T P _ {c} + F P _ {c} + F N _ {c}}. \tag {8}
+$$
+
+Following the definition of 3D occupancy prediction, we only evaluate our results in visible regions.
+
+Implementation details. We input six images of the original size into ResNet10 to obtain image features. Our image backbone is pre-trained on the FCOS3D [26], which is the same backbone used in the BEVFormer baseline. Then the features will be taken by FPN to produce multi-scale feature maps. The dimension of features is  $d = 256$ . We stacked three decoder layers, with point cross-attention and group point cross-attention in each decoder layer. There is a small MLP as our point-based occupancy predictor that projects the 256 feature dimension to the number of classes. We train our model on 8 NVIDIA 3090 GPUs with 24 epochs. We use the AdamW optimizer with a learning rate of  $2 \times 10^{-4}$  and a weight decay of 0.01. The learning rate of the backbone is 10 times smaller. Besides, we conducted a separate experiment in which we trained our decoder with a frozen BEVFormer baseline backbone using the same setting above and this decoder will be used as a plugin module for the BEVFormer baseline.
+
+Baseline methods. We compare Occupancy as Set of Points with existing methods replicated by Occ3D on Occ3D-nuScenes benchmark, including MonoScene [4], TPVFormer [9], BEVDet [7], OccFormer [32] and BEVFormer [15]. To make a fair comparison with the baseline, we provide results using BEVFormer by our implementation using the camera visible mask during the training.
+
+Table 1: 3D Occupancy prediction performance on the Occ3D-nuScenes dataset. * means the performance is achieved by our implementation using the camera mask during training. † means the performance is achieved with a frozen BEVFormer baseline backbone.  
+
+<table><tr><td>Method</td><td>others</td><td>barrier</td><td>bicycle</td><td>bus</td><td>car</td><td>Cons. Veh</td><td>motorcycle</td><td>pedestrian</td><td>traffic cone</td><td>trailer</td><td>truck</td><td>Dri. Sur</td><td>other flat</td><td>sidewalk</td><td>terrain</td><td>mammade vegetation</td><td>mIoU</td><td></td></tr><tr><td>MonoScene [4]</td><td>1.75</td><td>7.23</td><td>4.26</td><td>4.93</td><td>9.38</td><td>5.67</td><td>3.98</td><td>3.01</td><td>5.90</td><td>4.45</td><td>7.17</td><td>14.91</td><td>6.32</td><td>7.92</td><td>7.43</td><td>1.01</td><td>7.65</td><td>6.06</td></tr><tr><td>TPVFormer [9]</td><td>7.22</td><td>38.90</td><td>13.67</td><td>40.78</td><td>45.90</td><td>17.23</td><td>19.99</td><td>18.85</td><td>14.30</td><td>26.69</td><td>34.17</td><td>55.65</td><td>35.47</td><td>37.55</td><td>30.70</td><td>19.40</td><td>16.78</td><td>27.83</td></tr><tr><td>BEVDet [7]</td><td>4.39</td><td>30.31</td><td>0.23</td><td>32.26</td><td>34.47</td><td>12.97</td><td>10.34</td><td>10.36</td><td>6.26</td><td>8.93</td><td>23.65</td><td>52.27</td><td>24.61</td><td>26.06</td><td>22.31</td><td>15.04</td><td>15.10</td><td>19.38</td></tr><tr><td>OccFormer [32]</td><td>5.94</td><td>30.29</td><td>12.32</td><td>34.40</td><td>39.17</td><td>14.44</td><td>16.45</td><td>17.22</td><td>9.27</td><td>13.90</td><td>26.36</td><td>50.99</td><td>30.96</td><td>34.66</td><td>22.73</td><td>6.76</td><td>6.97</td><td>21.93</td></tr><tr><td>BEVFormer [15]</td><td>5.85</td><td>37.83</td><td>17.87</td><td>40.44</td><td>42.43</td><td>7.36</td><td>23.88</td><td>21.81</td><td>20.98</td><td>22.38</td><td>30.70</td><td>55.35</td><td>28.36</td><td>36.30</td><td>28.06</td><td>20.04</td><td>17.69</td><td>26.88</td></tr><tr><td>CTF-Occ [25]</td><td>8.09</td><td>39.33</td><td>20.56</td><td>38.29</td><td>42.24</td><td>16.93</td><td>24.52</td><td>22.72</td><td>21.05</td><td>22.98</td><td>31.11</td><td>53.33</td><td>33.84</td><td>37.98</td><td>33.23</td><td>20.79</td><td>18.0</td><td>28.53</td></tr><tr><td>SurroundOcc [29]</td><td>8.7</td><td>39.2</td><td>19.7</td><td>41.4</td><td>46.2</td><td>18.7</td><td>20.6</td><td>26.4</td><td>23.3</td><td>27.0</td><td>32.5</td><td>78.0</td><td>38.3</td><td>46.6</td><td>49.6</td><td>36.7</td><td>31.6</td><td>34.4</td></tr><tr><td>OpenOccupancy [27]</td><td>10.4</td><td>45.7</td><td>23.6</td><td>42.4</td><td>49.3</td><td>14.8</td><td>24.6</td><td>27.7</td><td>27.8</td><td>27.6</td><td>33.3</td><td>79.2</td><td>39.8</td><td>47.1</td><td>50.5</td><td>37.7</td><td>31.8</td><td>36.1</td></tr><tr><td>BEVDet-depth [8]</td><td>6.6</td><td>41.2</td><td>7.0</td><td>42.7</td><td>48.4</td><td>18.4</td><td>12.9</td><td>22.0</td><td>18.2</td><td>28.2</td><td>33.2</td><td>80.1</td><td>39.7</td><td>49.1</td><td>52.1</td><td>39.9</td><td>33.8</td><td>33.7</td></tr><tr><td>BEVDet-stereo [8]</td><td>8.6</td><td>45.9</td><td>14.3</td><td>46.0</td><td>51.2</td><td>23.8</td><td>18.9</td><td>24.1</td><td>22.3</td><td>33.6</td><td>37.9</td><td>81.5</td><td>40.5</td><td>52.6</td><td>55.9</td><td>46.9</td><td>41.2</td><td>38.0</td></tr><tr><td>BEVFormer* [15]</td><td>8.54</td><td>46.24</td><td>20.28</td><td>47.46</td><td>53.04</td><td>19.33</td><td>25.39</td><td>26.16</td><td>25.1</td><td>33.45</td><td>37.75</td><td>81.17</td><td>38.64</td><td>49.32</td><td>52.54</td><td>42.73</td><td>36.13</td><td>37.84</td></tr><tr><td>OSP</td><td>11.2</td><td>47.25</td><td>27.06</td><td>47.57</td><td>53.66</td><td>23.21</td><td>29.37</td><td>29.68</td><td>28.41</td><td>32.39</td><td>37.94</td><td>79.35</td><td>41.46</td><td>50.31</td><td>53.23</td><td>40.52</td><td>35.39</td><td>39.41</td></tr><tr><td>\( OSP^† \)</td><td>8.87</td><td>46.33</td><td>21.32</td><td>47.51</td><td>53.14</td><td>19.6</td><td>26.12</td><td>26.84</td><td>26.68</td><td>33.67</td><td>37.94</td><td>81.21</td><td>39.13</td><td>49.48</td><td>52.76</td><td>42.73</td><td>36.12</td><td>38.20</td></tr><tr><td>BEVFormer w/ \( OSP^† \)</td><td>10.95</td><td>49.0</td><td>27.68</td><td>50.24</td><td>55.99</td><td>22.96</td><td>31.02</td><td>30.91</td><td>30.25</td><td>35.6</td><td>41.23</td><td>82.09</td><td>42.59</td><td>51.9</td><td>55.1</td><td>44.82</td><td>38.17</td><td>41.21</td></tr></table>
+
+# 5.2 Performance
+
+Evaluation strategy. Our evaluation strategy for the method comprehensively utilized the three types of Points of Interest (PoIs) previously outlined. This approach entailed: (1) Standard Grids. We conducted sampling at the centers of grids, enabling us to benchmark our method's mIoU against traditional volume-based methods. This comparison provided a direct assessment of our method's performance in a standard scenario. (2) Adaptively Sampling. We adaptively oversample in our training phase to enhance our performance. Furthermore, we integrated our method as a complementary tool with volume-based approaches. By adaptively selecting PoIs that need refinement, our method enhances and augments existing volume-based techniques as shown in Fig. 3. (3) Manually Sampling. We manually select points out of the standard perception range and test out the model's perceptual ability.
+
+Through these varied and thorough evaluation techniques, we are able to comprehensively assess the performance and flexibility of our method across different scenarios and use cases.
+
+Standard grids. In Tab. 1, we benchmarked our method against existing camera-based 3D occupancy prediction techniques on the Occ3D-nuScenes benchmark by setting PoIs to standard grids. Our point-based approach achieved a notable improvement, obtains 1.57 mIoU performance gain compared to our implementation of the Bevformer baseline. Notably, our method outperformed the BEVFormer baseline across almost all category metrics, demonstrating a particularly clear advantage in detecting small targets, such as the bicycle  $(20.28 \rightarrow 27.06)$ , motorcycle  $(25.39 \rightarrow 29.37)$ , pedestrian  $(26.16 \rightarrow 29.37)$  and traffic cone  $(25.1 \rightarrow 28.31)$ . This advantage is mainly because the direct sampling of spatial points is beneficial for feature extraction and mapping of small objects.
+
+![](images/36c0684c0386e0388fcbe44d86f3de89e6b0d20b734825c9b09116904f526eff.jpg)  
+Fig. 3: Pipeline of refining volume-based methods with OSP. Given RGB images, 2D features are extracted by the frozen image backbone of the volume-based method which in our case is the BEVFormer baseline. We use the volume-based decoder to infer the entire scene, the point decoder to infer our selected 3D points, and combine the results of both using a weighted sum method.
+
+Adaptively Sampling. (1) The results shown in Tab. 4 demonstrate that our adaptively oversampling strategy improved the results from 38.01 to 38.48. (2) In the adaptive refinement experiment, we use our own implementation of the BEVFormer baseline as a representative of volume-based methods and enhance it with our method using different scales of PoIs by adaptively selecting locations with low confidence. Our model is trained with the frozen BEVFormer baseline backbone. The experimental results, as shown in Tab. 3, confirm that our plugin model significantly enhances BEVformer's performance. Notably, the BEVFormer w/ OSP results surpass those obtained from the two models operating independently. Furthermore, as we increase the size of the refined scene, the mIoU scores of BEVFormer w/ OSP also exhibit marked improvements, indicating that our refinement approach is very effective across various areas.
+
+Manually Sampling. The Occ3D-nuScenes dataset offers annotations across an  $[80m\times 80m]$  range. We manually set the points and annotations within a smaller  $[60m\times 60m]$  area and set PoIs to points out of it. To comprehensively evaluate our method, we conducted assessments across three distinct ranges: the standard  $[60m\times 60m]$  range, the full dataset range of  $[80m\times 80m]$ , and an intermediate-range spanning from  $60m$  to  $80m$ . Results shown in Tab. 2 demonstrate the capability of our method to make predictions at manually selected locations which is outside the predefined range in this case.
+
+Table 2: Predict regions beyond the scene. The model was trained on scenes within a  $60m \times 60m$  range, and evaluations were conducted on the training scenes, the entire size of the scene, and the distance from  $60m$  to  $80m$ .  
+
+<table><tr><td>Method</td><td>Scene</td><td>Range</td><td>mIoU</td></tr><tr><td>OSP</td><td>Standard</td><td>0m ~ 60m</td><td>42.29</td></tr><tr><td>OSP</td><td>Large</td><td>0m ~ 80m</td><td>29.00</td></tr><tr><td>OSP</td><td>Beyond</td><td>60m ~ 80m</td><td>23.00</td></tr></table>
+
+Table 3: Refine BEVFormer with our decoder.  $\dagger$  means our model is trained with a frozen BEVFormer baseline backbone. Refine scale refers to an area defined by the length multiplied by the width, centered around the self-vehicle. The entire scene spans a scale of  $80m\times 80m$ . As the refine scale increases, the mIoU also gradually increases.  
+
+<table><tr><td>Method</td><td>Refine Scale</td><td>mIoU</td></tr><tr><td>BEVFormer</td><td>-</td><td>37.84</td></tr><tr><td>\( OSP^† \)</td><td>-</td><td>38.20</td></tr><tr><td>BEVFormer w/ OSP</td><td>20m × 20m</td><td>38.20</td></tr><tr><td>BEVFormer w/ OSP</td><td>40m × 40m</td><td>39.35</td></tr><tr><td>BEVFormer w/ OSP</td><td>50m × 50m</td><td>40.32</td></tr><tr><td>BEVFormer w/ OSP</td><td>80m × 80m</td><td>41.21</td></tr></table>
+
+# 5.3 Ablation studies
+
+Model architecture. We conducted ablation experiments on the model structure, particularly exploring variations in the number of transformer layers and the adaptively oversampling strategy. The results of these experiments are presented in Tab. 4. Even without an oversampling strategy and with just a 3-layer transformer, our method's baseline already surpasses the BEVFormer baseline, advancing from 37.84 to 38.01 in mIoU. The oversampling strategy further amplifies this improvement by enhancing the connections between spatial points, culminating in an increased mIoU of 38.48.
+
+Table 4: Model architecture. To make a fair comparison with the baseline, we set the number of layers to three. The oversampling strategy brings  $0.47\mathrm{mIoU}$ .  
+
+<table><tr><td>Layer Num</td><td>Oversample</td><td>mIoU</td></tr><tr><td>1</td><td>-</td><td>36.76</td></tr><tr><td>3</td><td>-</td><td>38.01</td></tr><tr><td>3</td><td>✓</td><td>38.48</td></tr></table>
+
+2D image features. The performance of our method is heavily reliant on the quality of image features. To elucidate this dependency, we conducted ablation studies focusing on the output from the model's 'neck' component. These experiments demonstrated the substantial impact of using a multi-scale output from the FPN. This architectural choice boosts the model's performance, as evidenced by the increase in mIoU from 38.67 to 39.41 as shown in Tab. 5.
+
+Grid center sampling method. Our ablation experiments also investigated the technique for the grid center sampling method during training. While initially using grid center points as coordinates, we introduced a variation by adding random perturbations. The results, as detailed in Tab. 5, indicate the introduction of this disturbance to the grid center points elevates the mIoU to 38.67.
+
+Table 5: 2D image features and point perturbation. Increasing the multi-scale feature maps of images from 2 to 4 brings  $0.74\mathrm{mIoU}$ . We randomly applied perturbations of  $0.1m$  to the coordinates of each point in three directions. This strategy brings  $0.19\mathrm{mIoU}$  
+
+<table><tr><td>Multi-scale Images Features</td><td>Point Perturbation</td><td>mIoU</td></tr><tr><td>2</td><td>-</td><td>38.48</td></tr><tr><td>2</td><td>✓</td><td>38.67</td></tr><tr><td>4</td><td>✓</td><td>39.41</td></tr></table>
+
+Loss design. The task of 3D occupancy prediction bears similarities to segmentation tasks. In the context of the Occ3D-nuScenes dataset, there is a notable issue of class imbalance. To address this challenge, we opted to incorporate dice loss into our optimization strategy. The effectiveness of this choice is evident in the results presented in Tab. 6, which demonstrate that the integration of dice loss benefits our method.
+
+Table 6: Loss design. Incorporating dice loss for optimization brings  $0.53\mathrm{mIoU}$ .  
+
+<table><tr><td>Dice Loss</td><td>mIoU</td></tr><tr><td>-</td><td>38.88</td></tr><tr><td>✓</td><td>39.41</td></tr></table>
+
+Adaptive point sampling. During the inference process, we apply adaptive sampling (adaptively select points with high uncertainty) in BEVFormer w/ OSP and OSP. Adaptive sampling can reduce computational burden while maintaining good performance as shown in Tab. 7. BEVFormer w/ OSP using adaptive sampling means we refine BEVFormer by adaptively selecting points with high uncertainty (around  $20\%$ ). OSP using adaptive sampling means we only forward
+
+Table 7: Adaptability of OSP. 'rel' denotes relative.  
+
+<table><tr><td>Method</td><td>Adaptive Sampling</td><td>Rel. Computation</td><td>mIoU</td></tr><tr><td>BEVFormer</td><td>-</td><td>-</td><td>37.84</td></tr><tr><td>BEVFormer w/ OSP</td><td>-</td><td>1.0×</td><td>39.35</td></tr><tr><td>BEVFormer w/ OSP</td><td>✓</td><td>0.8×</td><td>39.22</td></tr><tr><td>OSP</td><td>-</td><td>1.0×</td><td>39.41</td></tr><tr><td>OSP</td><td>✓</td><td>0.93×</td><td>39.08</td></tr></table>
+
+points with low confidence to the next decoder layer, while directly outputting the results for points with high confidence in the decoder. Points of high uncertainty are defined as those with a confidence score less than a threshold of 0.9 after softmax.
+
+![](images/2daec395d6a850edda3f9b6943bfd379555023b28f066d76d77de987a8c88d00.jpg)  
+Fig. 4: Visualization of our results. Our visualization results have voids compared to the ground truth ego-vehicle position, which is due to the surrounding view having gaps near the ego-vehicle.
+
+# 6 Conclusion
+
+In this work, we present a novel perspective on 3D scene representation, viewing it through a set of points. We introduce the innovative concept of Points of Interest (PoIs), which significantly advances the flexibility in scene representation. Building upon the foundation laid by PoIs, we develop a highly adaptable, point-based 3D occupancy prediction framework, named OSP. We validate the strong performance and flexibility of OSP on the Occ3D-nuScenes benchmark. Our work not only contributes to the field of 3D occupancy prediction but also paves the way for more dynamic and adaptable methods in 3D scene analysis.
+
+# Acknowledgements
+
+This work was supported by National Natural Science Foundation of China (No. 62376102) and the Agricultural Science and Technology Independent Innovation Foundation of Jiangsu Province, China, No. CX(22)2031.
+
+# References
+
+1. Behley, J., Garbade, M., Milioto, A., Quenzel, J., Behnke, S., Stachniss, C., Gall, J.: Semantickitti: A dataset for semantic scene understanding of lidar sequences. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 9297-9307 (2019) 4  
+2. Caesar, H., Bankiti, V., Lang, A.H., Vora, S., Liong, V.E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., Beijbom, O.: nuscenes: A multimodal dataset for autonomous driving. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 11621-11631 (2020) 3  
+3. Cai, Y., Chen, X., Zhang, C., Lin, K.Y., Wang, X., Li, H.: Semantic scene completion via integrating instances and scene in-the-loop. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 324-333 (2021) 4  
+4. Cao, A.Q., de Charette, R.: Monoscene: Monocular 3d semantic scene completion. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3991-4001 (2022) 3, 4, 9, 10  
+5. Chen, X., Lin, K.Y., Qian, C., Zeng, G., Li, H.: 3d sketch-aware semantic scene completion via semi-supervised structure prior. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4193-4202 (2020) 4  
+6. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770-778 (2016) 6  
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+    {
+        "type": "text",
+        "text": "Octopus: Embodied Vision-Language Programmer from Environmental Feedback",
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+        "text": "Jingkang Yang\\*,1, Yuhao Dong\\*,2,3, Shuai Liu\\*,2,4, Bo Li\\*,1, Ziyue Wang\\*,1, Haoran Tan\\*,4, Chencheng Jiang\\*,5, Jiamu Kang\\*,3, Yuanhan Zhang\\*, Kaiyang Zhou\\* and and Ziwei Liu\\*",
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+        "text": "$^{1}$  S-Lab, Nanyang Technological University  $^{2}$  Shanghai AI Laboratory  $^{3}$  Tsinghua University  $^{4}$  BUPT  $^{5}$  XJTU  $^{6}$  Hong Kong Baptist University  $\\{\\text{jingkang001, ziwei.liu}\\}@\\text{ntu.edu.sg}$",
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+        "img_path": "images/6e1fffbae234e11e03cf9b934aa2f24e859d1db42aff5e8724d5471c83bc1950.jpg",
+        "image_caption": [
+            "Fig. 1: Illustration of our vision-language programmer, Octopus, complete a task in GTA environment. Given a task in the form of natural language, Octopus relies on its egocentric vision to generate plans and the corresponding executable code."
+        ],
+        "image_footnote": [],
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+        "text": "Abstract. Large vision-language models (VLMs) have achieved substantial progress in multimodal perception and reasoning. When integrated into an embodied agent, existing embodied VLM works either output detailed action sequences at the manipulation level or only provide plans at an abstract level, leaving a gap between high-level planning and real-world manipulation. To bridge this gap, we introduce Octopus, an embodied vision-language programmer that uses executable code generation as a medium to connect planning and manipulation. Octopus is designed to 1) proficiently comprehend an agent's visual and textual task objectives, 2) formulate intricate action sequences, and 3) generate executable code. To facilitate Octopus model development, we introduce OctoVerse: a suite of environments tailored for benchmarking",
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+    {
+        "type": "page_footnote",
+        "text": "* Equal contribution, †Equal engineering contribution, ⌿ Corresponding author.",
+        "bbox": [
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+        ],
+        "page_idx": 0
+    },
+    {
+        "type": "text",
+        "text": "vision-based code generators on a wide spectrum of tasks, ranging from mundane daily chores in simulators to sophisticated interactions in complex video games such as Grand Theft Auto (GTA) and Minecraft. To train Octopus, we leverage GPT-4 to control an explorative agent that generates training data, i.e., action blueprints and corresponding executable code. We also collect feedback that enables an enhanced training scheme called Reinforcement Learning with Environmental Feedback (RLEF). Through a series of experiments, we demonstrate Octopus's functionality and present compelling results, showing that the proposed RLEF refines the agent's decision-making. By open-sourcing our simulation environments, dataset, and model architecture, we aspire to ignite further innovation and foster collaborative applications within the broader embodied AI community. The project page is available at https://choiszt.github.io/0ctopus/.",
+        "bbox": [
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+        "page_idx": 1
+    },
+    {
+        "type": "text",
+        "text": "1 Introduction",
+        "text_level": 1,
+        "bbox": [
+            215,
+            368,
+            375,
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+        ],
+        "page_idx": 1
+    },
+    {
+        "type": "text",
+        "text": "The rise of large language models (LLMs) [11,15,42,46,54] led to a surge in vision-language models (VLMs) [4,5,31,33,35-37], enabling tasks such as image/video-based descriptions [33], reasoning [14,38,60], and conversations [16,31]. In the realm of embodied AI, notable efforts [10,18,33] have trained agents to process visual input and relay motor control commands.",
+        "bbox": [
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+        "page_idx": 1
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+    {
+        "type": "text",
+        "text": "Another approach to interacting with the environment focuses on task execution through code invocations, mirroring the human System-I stimulation [19,29] (automatic, intuitive actions) with predefined code, and leaving the System-II processes [19,29] (planning and reasoning) for large models. For example, referring to Fig. 1, planning a car ride with a pet might entail a subconscious checklist (e.g., getOutOf() the house, open() the car door), each action could be implemented using specific techniques [8,24] such as imitation learning [22,28]. This programmatic paradigm has been, although not in vision, leveraged by works [25, 50, 52, 53] using LLMs to craft programs and trigger APIs. Game-centric models like Voyager [55] have similarly employed GPT for function calls within game engines, though they often parse data directly from their environments.",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "However, when incorporating visual perception, the programming paradigms are largely unexplored. Primary initiatives [48,58] can only output plans, which anchor their strategies only in initial environmental states or employ dynamic scene graphs for LLM inputs, respectively. Despite their innovations, the overreliance on pre-trained vision models to convert vision content into language can occasionally hinder the LLM's planning performance. The conversion from plans into real-world actions is still missing. While EmbodiedGPT [41] addresses the problem by integrating vision-language modeling for planning and then transitioning to manipulation using policy mapping, the capability of embodied vision-language models to generate executable programs remains largely uncharted.",
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+    {
+        "type": "text",
+        "text": "Our exploration aims to bridge this gap. An embodied vision-language programmer should integrate visual perspective with textual objectives to devise",
+        "bbox": [
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+        "page_idx": 1
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+    {
+        "type": "page_number",
+        "text": "2",
+        "bbox": [
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+        "page_idx": 1
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+    {
+        "type": "header",
+        "text": "J. Yang et al.",
+        "bbox": [
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+        ],
+        "page_idx": 1
+    },
+    {
+        "type": "text",
+        "text": "action plans and executable code (Fig. 1). However, existing simulator environments often lack the carefully designed functions necessary to support such models effectively. These functions should balance usefulness and complexity to avoid hindering the development of genuine embodied vision-language programmers. For example, the **explore_until()** function in Minecraft, which can lead the player directly to specific blocks without relying on vision information, may not be suitable for training these models.",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "To meet the requirement, we carefully design and develop OctoVerse, a suite of environments consisting of diverse simulators, including (i) OctoGibson, built upon the photorealistic OmniGibson [32], (ii) OctoMC, developed on the infinitely creative, pixel-style Minecraft platform [2], and (iii) OctoGTA, adapted from the highly interactive and immersive Grand Theft Auto V (GTA-V) [1]. These environments enable the training and benchmarking of our embodied vision-language programming model in a wide range of scenarios, from daily household tasks to complex urban navigation and open-world exploration, while the function calls are tailored to be vision-dependent.",
+        "bbox": [
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+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "Using the OctoVerse environment, we train Octopus by leveraging GPT-4 to collect data. We provide GPT-4 with system messages, environmental cues, and objectives, enabling it to formulate action strategies and code. Simultaneously, the agent captures visual perspectives, forming the image-code pair for Octopus training. During data collection, the agent receives simulator feedback, distinguishing successful moves from unsuccessful ones. We incorporate this feedback using Reinforcement Learning with Environmental Feedback (RLEF) and fine-tune Octopus using Proximal Policy Optimization (PPO) [51]. Empirically, Octopus demonstrates strong adaptability in various scenarios, outperforming existing models in task planning, code generation, and execution. The integration of RLEF further enhances Octopus's performance, showcasing the effectiveness of this training approach. In sum, our key contributions include:",
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+    {
+        "type": "list",
+        "sub_type": "text",
+        "list_items": [
+            "- A Novel Vision-Language Programming Benchmark: Three diverse embodied environments with designed tasks: (i) OctoGibson, which is developed upon OmniGibson [32], (ii) OctoMC that developed on Minecraft [2], and (iii) OctoGTA, which is adapted from GTA-V [1].",
+            "- A New Vision-Language Programming Model: An embodied vision-language planner and programmer trained with Reinforcement Learning with Environmental Feedback (RLEF), demonstrates compelling results.",
+            "- Insights on Vision-Language Programming: We extensively explore Octopus and share useful insights facilitating future research on visual planning and programming."
+        ],
+        "bbox": [
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+        "page_idx": 2
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+    {
+        "type": "text",
+        "text": "2 Related Work",
+        "text_level": 1,
+        "bbox": [
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+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "2.1 Embodied AI Simulators",
+        "text_level": 1,
+        "bbox": [
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+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "Embodied AI has advanced significantly with the development of diverse simulation environments, enabling research tasks such as visual exploration [47],",
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+        "page_idx": 2
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+    {
+        "type": "header",
+        "text": "Octopus: Embodied VLM from Environmental Feedback",
+        "bbox": [
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+        "page_idx": 2
+    },
+    {
+        "type": "page_number",
+        "text": "3",
+        "bbox": [
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+    },
+    {
+        "type": "table",
+        "img_path": "images/c5542a1160ed522d15337fac866fb91a9da23c4f1225a38aa8ecc7ceb793c5d6.jpg",
+        "table_caption": [
+            "Table 1: Related Work for OctoVerse - Overview of Embodied AI Environments. We select three environments into OctoVerse and carefully design executable tasks and vision-dependent function calls (VC), in comparison to undesigned standard function calls (C)."
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Simulator</td><td>Kinematics</td><td>Continuous \nExtended \nStates</td><td>Flexible \nMaterials</td><td>Deformable \nBodies</td><td>Realistic \nAction \nExecution</td><td>Game- or \nWorld-Based</td><td>Formulated \nTasks</td><td>Function \nCall \nType</td></tr><tr><td>OpenAIGym [9]</td><td>✓</td><td>×</td><td>×</td><td>×</td><td>✓</td><td>G</td><td>×</td><td>C</td></tr><tr><td>Matterport3D [12]</td><td>×</td><td>×</td><td>×</td><td>×</td><td>×</td><td>W</td><td>×</td><td>×</td></tr><tr><td>AI2THOR [30]</td><td>✓</td><td>×</td><td>×</td><td>×</td><td>×</td><td>G</td><td>×</td><td>C</td></tr><tr><td>VirtualHome [44]</td><td>×</td><td>×</td><td>×</td><td>×</td><td>×</td><td>G</td><td>×</td><td>×</td></tr><tr><td>House3D [57]</td><td>×</td><td>×</td><td>×</td><td>×</td><td>×</td><td>W</td><td>×</td><td>×</td></tr><tr><td>Habitat 1.0 [49]</td><td>✓</td><td>×</td><td>×</td><td>×</td><td>✓</td><td>W</td><td>×</td><td>C</td></tr><tr><td>Robosuite [66]</td><td>✓</td><td>×</td><td>×</td><td>×</td><td>✓</td><td>W</td><td>×</td><td>C</td></tr><tr><td>RFUniverse [21]</td><td>✓</td><td>×</td><td>✓</td><td>✓</td><td>✓</td><td>W</td><td>×</td><td>C</td></tr><tr><td>Minecraft [2]</td><td>✓</td><td>×</td><td>✓</td><td>×</td><td>✓</td><td>G</td><td>×</td><td>C</td></tr><tr><td>OctoMC</td><td>✓</td><td>×</td><td>✓</td><td>×</td><td>✓</td><td>G</td><td>✓</td><td>VC</td></tr><tr><td>GTA [1]</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>G</td><td>×</td><td>C</td></tr><tr><td>OctoGTA</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>G</td><td>✓</td><td>VC</td></tr><tr><td>OmniGibson [32]</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>W</td><td>×</td><td>C</td></tr><tr><td>OctoGibson</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>W</td><td>✓</td><td>VC</td></tr></table>",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "navigation [56], and question-answering [17]. Several simulators, including AI2-THOR [30], VirtualHome [44], Habitat-Sim [49], SAPIEN [59], and Omnigibson [32], provide realistic representations of the world for investigating embodied AI challenges. OmniGibson [32] stands out for its high-fidelity simulation of diverse indoor and outdoor environments. OctoGibson environment further enhances OmniGibson with carefully designed function calls and formulated tasks, making it well-suited for vision-language programming.",
+        "bbox": [
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+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "Game-related simulators like Arade [7], CHALET [61], and VRKitchen [23] also contribute significantly to embodied AI. Minecraft [2] has gained attention in reinforcement learning and game agents [6,20,39,55,63,64] but lacks the necessary structure for vision-language programming. OctoMC addresses this by providing designed function calls and formulated tasks. In contrast to Minecraft's voxel-based representations that limit transferability to real-world environments, GTA-V [1] offers a highly realistic environment. In this work, we introduce OctoGTA as a new setting, leveraging GTA-V's rich, open-world environment with incorporated tasks and function calls, extending this platform for embodied AI study.",
+        "bbox": [
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+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "2.2 Embodied AI with Large Models",
+        "text_level": 1,
+        "bbox": [
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+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "The recent wave of research focuses on merging LLMs with embodied AI tasks [11, 42,46,54]. For instance, VoxPoser addresses robotic manipulation problems through unsupervised methods [27]. A group of projects, namely SayCan [3], Palm-E [18], RT-2 [10], and EmbodiedGPT [41], effectively integrate visual or linguistic cues with robot manipulation data. Outside the domain of robotic manipulation, initiatives like Voyager [55] and Smallville [43] harness the capabilities of GPT to interface with game functions, relying on preset functions to manage intricate manipulations. In a parallel vein, VisProg [25] leverages GPT-3 language prompts to craft Python programs, opening the door to a multitude of fascinating applications. While the proposed Octopus model also formulates plans",
+        "bbox": [
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+        "page_idx": 3
+    },
+    {
+        "type": "page_number",
+        "text": "4",
+        "bbox": [
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+        "page_idx": 3
+    },
+    {
+        "type": "header",
+        "text": "J. Yang et al.",
+        "bbox": [
+            271,
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+            128
+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "table",
+        "img_path": "images/cf730938763d2c71668e4f6703713e8b17853f2b0bf5c5ec1b5df819afe6c5e4.jpg",
+        "table_caption": [
+            "Table 2: Related Work for Octopus - Overviewing Embodied AI Models. The proposed Octopus distinguishes itself from other models as a unified vision-language model for both plan and code generation."
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Models</td><td>Release Date</td><td>Supported Environment</td><td>Vision Model</td><td>Code Generator</td><td>Action w/ Feedback</td><td>LLM Training Enabled</td></tr><tr><td>Text2Motion [34]</td><td>Mar. 2023</td><td>Sim</td><td>×</td><td>✓</td><td></td><td>×</td></tr><tr><td>Instruct2Act [26]</td><td>May 2023</td><td>Sim</td><td>×</td><td>✓</td><td></td><td>×</td></tr><tr><td>Lang2Rewards [62]</td><td>Jun. 2023</td><td>Sim</td><td>×</td><td>✓</td><td></td><td>×</td></tr><tr><td>VoxPoser [27]</td><td>Jul. 2023</td><td>Sim</td><td>✓</td><td>×</td><td></td><td>×</td></tr><tr><td>SayCan [3]</td><td>Apr. 2022</td><td>Real</td><td>✓</td><td>×</td><td></td><td>×</td></tr><tr><td>PALM-E [18]</td><td>Mar. 2023</td><td>Sim, Real</td><td>✓</td><td>×</td><td></td><td>✓</td></tr><tr><td>RT-2 [10]</td><td>Jul. 2023</td><td>Real</td><td>✓</td><td>×</td><td></td><td>✓</td></tr><tr><td>SayPlan [48]</td><td>Jun. 2023</td><td>Real</td><td>×</td><td>×</td><td></td><td>×</td></tr><tr><td>EmbodiedGPT [41]</td><td>May 2023</td><td>Sim</td><td>✓</td><td>×</td><td></td><td>✓</td></tr><tr><td>TaPA [58]</td><td>Jul. 2023</td><td>Sim</td><td>×</td><td>×</td><td></td><td>✓</td></tr><tr><td>Voyager [55]</td><td>May 2023</td><td>Game</td><td>×</td><td>✓</td><td></td><td>×</td></tr><tr><td>Steve-Eye [64]</td><td>Dec 2023</td><td>Game</td><td>✓</td><td>×</td><td></td><td>✓</td></tr><tr><td>RoboScript [13]</td><td>Feb 2024</td><td>Sim, Real</td><td>×</td><td>✓</td><td></td><td>×</td></tr><tr><td>Octopus</td><td>-</td><td>Sim, Game</td><td>✓</td><td>✓</td><td></td><td>✓</td></tr></table>",
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+    {
+        "type": "text",
+        "text": "and code, its distinguishing feature is the seamless integration of visual input in program and code generation. This also stands in contrast to other embodied planners like TAPA [58] and SayPlan [48], which deploy separate vision modules to translate visual data into linguistic inputs for LLMs. Octopus excels as a cohesive vision-language model, delivering not just plans but also code.",
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+    {
+        "type": "text",
+        "text": "More discussion on additional related works (Vision Language Model, and Feedback in Large Language Models) is included in the supplementary materials.",
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+    {
+        "type": "text",
+        "text": "3 The OctoVerse Environment",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "In this section, we introduce three simulator environments designed to train and evaluate the Octopus model. For each environment, we will describe their overall information, the special design considerations that ensure the tasks are well-formulated and the callable functions are vision-dependent.",
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+    {
+        "type": "text",
+        "text": "OctoGibson We built the environment on the foundation of OmniGibson [32], an existing simulation framework that supports 1,000 daily activities across 50 scenes, featuring over 5,000 meticulously annotated objects. We incorporated 16 functions that the robot can execute, such as moveBot() and easyGrasp(). Within this environment, we meticulously crafted 476 tasks<sup>1</sup>, each with a well-defined initial state and a definitive termination state, allowing for a straightforward assessment of task completion. Among these tasks, 367 are routine tasks—simple and direct actions like \"place a glass in a trash can,\" marked as Follow. Conversely, the remaining 109 are reasoning tasks that necessitate deeper comprehension. An example is \"buy a chocolate,\" where the agent needs",
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+    {
+        "type": "header",
+        "text": "Octopus: Embodied VLM from Environmental Feedback",
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+    },
+    {
+        "type": "page_number",
+        "text": "5",
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+    {
+        "type": "page_footnote",
+        "text": "1 The full list of tasks and their categories are listed in the supplementary material.",
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+    {
+        "type": "text",
+        "text": "to know to pick a chocolate bar from the shelf and then place it, along with money, on the checkout counter, denoted as Reason tasks.",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "To ensure our tasks are vision-aware, we deliberately constrain the usage of certain functions, such as moveBot(object), which moves the agent in front of the given object. To avoid making the task too easy and vision-agnostic, we limit the given parameter to a predefined set of large objects, such as tables and cupboards, rather than small items like cups and glasses. In this case, if the robot wants to pick up a cup, it needs to recognize whether the cup is on the table or in the cupboard. A simple moveBot(object) call with an inappropriate parameter would cause a runtime error. The full list of the functions is in the Appendix.",
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+        "text": "OctoMC The OctoMC environment is built on Minecraft [2], a popular platform for reinforcement learning and game agents. We integrated 6 functional actions and crafted 40 tasks $^{2}$ , each designed to facilitate comprehensive observations and executions by the agent. These tasks are distributed across 10 different biomes, including indoor, outdoor, and underground settings, under varying weather conditions.",
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+        "type": "text",
+        "text": "However, existing Minecraft environments often lack vision-formulated tasks and the necessary structure for vision-language programming. For example, in Voyager [55], the exploreUntil() function allows the player to navigate directly to specific blocks without relying on vision information. This function works by randomly exploring the environment within a certain range until the desired object is found, at which point it returns a true value, enabling the agent to interact with the located object, such as gold blocks or trees. While effective, this approach is entirely automated and does not utilize visual information, making it unsuitable for our vision-based objectives.",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "To address this limitation, we crafted a vision-dependent exploration function, teleport(yaw, distance), which operates within the robot's perceptual range. This function ensures that the agent's operations are vision-dependent and require active perception and navigation within the environment. For instance, to locate a tree block, the agent must actively navigate towards the direction of the forest, relying on visual cues to guide its exploration.",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "OctoGTA The OctoGTA environment is built on GTA-V [1] with the help of the active GTA modding community. We integrated 19 functions and methodically crafted 25 tasks<sup>3</sup>, such as \"help NPC drive their boat back to shore\" and \"mediate a fight between two NPCs,\" spanning across 5 groups (shown in Fig. 2 (b)). Each task is assigned to 5 different locations within the game world.",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "We have implemented a set of functions that enable the agent to interact with the game world in a visually-aware manner. Similar to the design in Minecraft, we get rid of functions like walkTo location) that might trivialize the task of reaching a particular building or landmark. Instead, we provide functions such",
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+    {
+        "type": "page_number",
+        "text": "6",
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+    {
+        "type": "header",
+        "text": "J. Yang et al.",
+        "bbox": [
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+    {
+        "type": "page_footnote",
+        "text": "2 Detailed tasks are listed in the supplementary material.",
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+    {
+        "type": "page_footnote",
+        "text": "3 We meticulously designed tasks to be friendly, ensuring they exclude any inappropriate or violent behaviors.",
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+        "type": "image",
+        "img_path": "images/76f8463ebcdb002f43f190e5dcf9ce9a13839fdab317d9567151f0cc97fd2181.jpg",
+        "image_caption": [
+            "Fig. 2: The Statistics of the OctoVerse Environment with Function Designs."
+        ],
+        "image_footnote": [],
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+    {
+        "type": "text",
+        "text": "as goForward(distance) and turnPlayer(degree) (Fig. 2 (b)). For tasks like mediating a fight between two NPCs, the essential function stopFight() only works when the player is within 5 meters of the fighting NPCs. These design choices ensure that the agent's operations are vision-dependent and require active perception and navigation within the environment.",
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+        "type": "text",
+        "text": "4 Octopus: The Embodied Vision-Language Programmer",
+        "text_level": 1,
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+        "text": "In this section, we present the procedure for training Octopus. Starting from collecting training data within the OctoVerse environment, Octopus builds upon a VLM architecture of Otter [31] and includes specialized RLEF modules to handle vision-language programming tasks. Fig. 4 illustrates the entire Octopus training pipeline.",
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+        "text": "4.1 Training Data Collection",
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+        "type": "text",
+        "text": "We use the automatic training data collection pipeline described here for OctoGibson and OctoMC, with the latter using customized prompts inspired by Voyager [55]. For OctoGTA, we rely on human labor to hand-craft the training dataset due to the difficulty of obtaining textual environment messages in the GTA environment. In the following parts, we use OctoGibson as the primary example to illustrate the data collection pipeline. Note that the primary task in organizing training data is to form a succinct pairing: \"vision input + current/historical states  $\\rightarrow$  next step plan + executable code\".",
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+        "type": "text",
+        "text": "Environment Info Collection As shown in Fig. 4 (a) and Fig. 3, we format an environment message for each state, encompassing attributes like Observed Objects, Observed Relations, Inventory, and more. Specifically, the simulator can provide us with an exact scene graph at each state, shaping the content for the first two parts. The inventory info is also accessible. The task, e.g., \"cooking bacon\" in Fig. 3, is represented by the Task Goal.",
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+        "type": "text",
+        "text": "Automation with GPT-4 After preparing the environment message, we crafted a structured system message to ensure that the robot not only understands its input but also maintains a consistent output format. A detailed",
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+    {
+        "type": "header",
+        "text": "Octopus: Embodied VLM from Environmental Feedback",
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+        "page_idx": 6
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+    {
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+        "text": "7",
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+    {
+        "type": "image",
+        "img_path": "images/6cdd976d7b1070035e63f9f0eb63fef5fd12684a6160ac0c712a8b05aaf85f52.jpg",
+        "image_caption": [
+            "Fig.3: Data Collection Example for \"Cook a Bacon\" Task. GPT-4 perceives the environment through the environmental message and produces anticipated plans and code following the detailed system message. This code is subsequently executed in the simulator, directing the agent to the subsequent state. For each state, we gather the environmental message, wherein observed objects and relations are substituted by egocentric images to serve as the training input. The response from GPT-4 acts as the training output. Environmental feedback, specifically the determination of whether each target state is met, is documented for RLEF training."
+        ],
+        "image_footnote": [],
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+    {
+        "type": "text",
+        "text": "examination of this prompt can be found in the appendix. Experiments have shown that a well-articulated prompt enables GPT-4 to effectively generate executable code. It is important to note that the combined length of the system and environment messages can be extremely long, which may cause standard GPT-4 8K models to struggle with producing meaningful outputs. To address this issue, we employ the more robust GPT-4 32K model. As illustrated in Fig. 3, when GPT-4 receives a consistent system and environment message, it generates comprehensive outputs that include current scenario analysis, planning, and actionable code, supporting the training process in Section 4.3.",
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+    {
+        "type": "text",
+        "text": "Error Management Notably, GPT-4 collects training data under the main task of guiding the agent to complete tasks. However, GPT-4 is not infallible. Errors can manifest in multiple ways, ranging from syntax errors to physical challenges in the simulator. For instance, in Fig. 3, between states  $\\#5$  and  $\\#6$ , the action failed due to the long distance between the agent (holding bacon) and the pan. Such setbacks reset the task to its previous state. If a main task remains incomplete after 10 steps, it is deemed unsuccessful, and we terminate this task for budget concerns. However, all data pairs without syntax errors, regardless of the task's completion status, are valuable for refining instructions and improving the model's performance.",
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+    {
+        "type": "text",
+        "text": "Environmental Feedback GPT-4's continual trial-and-error approach while guiding the agent toward task completion serves a dual purpose: collecting vision-",
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+        "text": "8",
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+    {
+        "type": "header",
+        "text": "J. Yang et al.",
+        "bbox": [
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+        "page_idx": 7
+    },
+    {
+        "type": "image",
+        "img_path": "images/e7e886cf6a620a9c98069660b102aace976d3cf7ca9f6b7a9e3ddf03c6685308.jpg",
+        "image_caption": [
+            "(a) Data Collection Pipeline"
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+    {
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+        "img_path": "images/d0ff0b0f34aadd20b48a71ac7fdbaec50a85b8bdbcf92072cdeb9a257db8ea84.jpg",
+        "image_caption": [
+            "(b) Octopus Training Pipeline"
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+        "img_path": "images/2b87a40202ac8da67bda6e6d35469e9fe011aa49fa4927b481cae1065f0d6289.jpg",
+        "image_caption": [
+            "(c) Octopus (Otter model)"
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+        "img_path": "images/65b407683e8cb947939a9954f9b85688b2bf07e5c7b7867d83395650594fc61d.jpg",
+        "image_caption": [
+            "Step 2: Training Reward Model"
+        ],
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+        "bbox": [
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+    {
+        "type": "image",
+        "img_path": "images/d0606d05feeaff5577056bd7d0673252451a64569f06890ba05703f7fdf75430.jpg",
+        "image_caption": [
+            "(d) RLEF Training",
+            "Fig. 4: How to train Octopus: data collection and training pipeline."
+        ],
+        "image_footnote": [],
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "output pairs and generating a rich set of feedback data. The automatic annotation of this feedback focuses on two levels: step-level and task-level judgments. Step-level judgment assesses the alignment of post-execution states with their target states. For instance, in Fig. 3, steps color-coded in green lead to positive feedback. One can visualize the action sequence for task completion as a tree, where each node indicates a step (subtask), encapsulating an action code. Accompanying each step is a binary value that denotes success or failure, giving preference to the successful branch over its counterpart. Task-level judgment, on the other hand, gauges the successful execution of the overall task. If the task is not completed as intended, every state within that task is labeled as negative, regardless of the status of the subtasks. This collated feedback data serves as a foundation for our Reinforcement Learning with Environmental Feedback (RLEF) methodology, which we discuss in greater detail in Section 4.4.",
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+    {
+        "type": "text",
+        "text": "4.2 Model Architecture",
+        "text_level": 1,
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+        "type": "text",
+        "text": "The Octopus architecture (shown in Fig. 4 (c)) is heavily inspired by the Otter model [31], integrates the MPT-7B Language Decoder [40] and CLIP VIT-L/14 Vision Encoder [45]. Adopting design principles from Flamingo [4], Octopus employs the Perceiver Resampler and Cross-Gated Attention modules to enhance vision-language synergy. This architecture enables Octopus to excel in tasks requiring understanding of both visual and textual data. The Octopus is also compatible with other VLMs such as LLaVA [37].",
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+        "text": "4.3 SFT: Supervised Finetuning with Instructions",
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+        "type": "text",
+        "text": "We train the Octopus model on our collected dataset from OctoVerse  $\\mathcal{D}_{\\mathrm{E}} = \\{(\\mathbf{X}_v,\\mathbf{T}_i,\\mathbf{T}_r)\\}$  using token-level supervised fine-tuning (SFT) [42,54]. The Perceiver Resampler transforms images  $\\mathbf{X}_v$  into visual tokens that condition subsequent layers via Cross-Gated Attention modules. The training objective is next-token prediction, modeling the likelihood of a targeted response  $\\mathbf{T}_r$  as:",
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+    {
+        "type": "equation",
+        "text": "\n$$\np \\left(\\mathbf {T} _ {r} \\mid \\mathbf {T} _ {i}, \\mathbf {X} _ {v}\\right) = \\prod_ {l = 1} ^ {L} p \\left(t _ {l} \\mid \\mathbf {X} _ {v}, \\mathbf {T} _ {i}, \\mathbf {T} _ {r, <   l}\\right). \\tag {1}\n$$\n",
+        "text_format": "latex",
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+    },
+    {
+        "type": "header",
+        "text": "Octopus: Embodied VLM from Environmental Feedback",
+        "bbox": [
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+    },
+    {
+        "type": "page_number",
+        "text": "9",
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+    {
+        "type": "text",
+        "text": "Note that  $\\mathbf{T}_i$  denotes the instruction tokens and  $\\mathbf{T}_{r, < l}$  denotes the response tokens before the current predicted token  $t_l$ . During inference, tokens are converted into natural language via the language decoder's text tokenizer.",
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "Visual observations  $\\mathbf{X}_v = \\{x_F^0,\\dots ,x_F^7,x_B^0,x_B^1\\}$  consist of 8 first-person view (FPV) images and two bird's-eye view (BEV) images for OctoGibson and OctoGTA. OctoMC only takes 4 FPV images. The FPV captures the agent's direct observations, while the BEV provides a holistic understanding of the environment. The eight FPV images are captured every 45 degrees, ensuring a complete 360-degree perspective.",
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+    {
+        "type": "text",
+        "text": "4.4 RLEF: Reinforcement Learning with Environmental Feedback",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "In OctoVerse, task progression can be visualized as a tree (Fig. 4 (d)), where each node represents a sub-task with a binary value indicating success (1) or failure (0). If a node (or sub-task) has a value of 1, it is a step in the correct direction toward our end goal.",
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+    {
+        "type": "text",
+        "text": "Tree-based Task Representation According to the environmental feedback part in Sec. 4.1, environmental reward datasets  $\\mathcal{D}_{\\mathbb{R}} = (\\mathbf{X}_v^*,\\mathbf{T}_i^*,\\mathbf{T}_r^i,\\mathbf{T}_r^j,c)$  are organized, where  $\\mathbf{T}_r^i$  and  $\\mathbf{T}_r^j$  are responses sharing the same parent task  $\\mathbf{T}_i^*$ , and  $c$  indicates the preferred response leading to task completion. This ensures the reward mechanism favors the successfully executed branch. Note that even if a parental node does not have multiple responses, we can still assign feedback according to the rule in Sec. 4.1.",
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+    {
+        "type": "text",
+        "text": "Reward Model Configuration A single-modal CodeLLaMA-7B model with an additional value head is fine-tuned on  $\\mathcal{D}_{\\mathrm{R}}$  as the reward model  $r_{\\phi}$ . This text-based model assesses state transitions ( $\\mathbf{T}_i^* \\rightarrow \\mathbf{T}_r^{i,j}$ ) to determine high-reward transitions, assisting the agent in task execution and completion. The rationale for using CodeLLaMA as the reward model is that evaluating rewards can be purely dependent on the textual output. Furthermore, CodeLLaMA's strong programming skills make it well-suited for assessing the quality and effectiveness of the generated code in the context of task completion.",
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+    {
+        "type": "text",
+        "text": "Policy Model Development The supervised fine-tuned model serves as the initial policy model  $\\pi^{\\mathrm{INIT}}$  with fixed parameters. A duplicate model,  $\\pi_{\\theta}^{\\mathrm{RL}}$ , is initialized and trained using Proximal Policy Optimization (PPO) [51] to maximize response rewards. The loss function is:",
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+    },
+    {
+        "type": "equation",
+        "text": "\n$$\n\\mathcal {L} \\left(\\pi_ {\\theta} ^ {\\mathrm {R L}}\\right) = - \\mathbb {E} _ {\\left(\\mathbf {X} _ {v} ^ {*}, \\mathbf {T} _ {i} ^ {*}\\right) \\in \\mathcal {D} _ {\\mathrm {R}}, \\mathbf {T} _ {r} \\sim \\pi^ {\\mathrm {R L}}} \\left[ r _ {\\phi} \\left(\\mathbf {T} _ {i} ^ {*}, \\mathbf {T} _ {r}\\right) - \\beta \\cdot \\mathbb {D} _ {\\mathrm {K L}} \\left(\\pi_ {\\theta} ^ {\\mathrm {R L}} \\left(\\mathbf {X} _ {v} ^ {*}, \\mathbf {T} _ {i} ^ {*}\\right) \\| \\pi^ {\\mathrm {I N I T}} \\left(\\mathbf {X} _ {v} ^ {*}, \\mathbf {T} _ {i} ^ {*}\\right)\\right) \\right],\n$$\n",
+        "text_format": "latex",
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+    {
+        "type": "text",
+        "text": "where  $\\beta$  acts as a hyper-parameter to regulate the magnitude of the Kullback-Leibler (KL) penalty.",
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+    {
+        "type": "text",
+        "text": "5 Experiments",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "5.1 Main Results on OctoGibson",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "Experimental Setup We first set up the OctoGibson to evaluate the performance of Octopus and other related models. Specifically, we are utilizing the",
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+    },
+    {
+        "type": "page_number",
+        "text": "10",
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+    },
+    {
+        "type": "header",
+        "text": "J. Yang et al.",
+        "bbox": [
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+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "table",
+        "img_path": "images/a4ad0b6a9dc17f8ac4c7399acb28f0bcdf7615a6395a1567d34ae72bec4be958.jpg",
+        "table_caption": [
+            "Table 3: Main Results on OctoGibson. We compare various models: standalone language models, adapted vision-language planners, and our Octopus models, across different evaluation settings. In cells displaying two values, the first represents the task completion rate across the target validation task sets, while the second assesses the conceptual accuracy of the model's planning as judged by human evaluators. GT denotes that the model input is directly parsed from the simulator, with information on objects (O) or relations (R). Octopus shows consistent advance in task completion."
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td rowspan=\"2\">Model</td><td rowspan=\"2\">Vision Model</td><td rowspan=\"2\">Language Model</td><td colspan=\"6\">Entire Goal Task</td></tr><tr><td>Seen Env</td><td>Unseen Env</td><td>Follow</td><td>Reason</td><td>All</td><td></td></tr><tr><td>GPT-4</td><td>-</td><td>-</td><td>0.42 / 0.69</td><td>0.46 / 0.67</td><td>0.49 / 0.78</td><td>0.27 / 0.40</td><td>0.43 / 0.68</td><td></td></tr><tr><td>GPT-4V</td><td>-</td><td>-</td><td>0.40 / 0.62</td><td>0.60 / 0.67</td><td>0.42 / 0.67</td><td>0.53 / 0.53</td><td>0.45 / 0.63</td><td></td></tr><tr><td>LLaMA</td><td>GT (O+R)</td><td>LLaMA2-7B</td><td>0.07 / 0.11</td><td>0.13 / 0.13</td><td>0.11 / 0.16</td><td>0.00 / 0.00</td><td>0.08 / 0.12</td><td></td></tr><tr><td>CodeLLaMA</td><td>GT (O+R)</td><td>CodeLLaMA-7B</td><td>0.09 / 0.20</td><td>0.20 / 0.40</td><td>0.16 / 0.31</td><td>0.00 / 0.07</td><td>0.12 / 0.25</td><td></td></tr><tr><td>TAPA (task-level)</td><td>QvD GT (O)</td><td>CodeLLaMA-7B</td><td>0.09 / 0.36</td><td>0.13 / 0.33</td><td>0.11 / 0.36</td><td>0.06 / 0.33</td><td>0.10 / 0.35</td><td></td></tr><tr><td>TAPA (step-level)</td><td>QvD GT (O)</td><td>CodeLLaMA-7B</td><td>0.16 / 0.42</td><td>0.13 / 0.27</td><td>0.18 / 0.38</td><td>0.07 / 0.40</td><td>0.15 / 0.38</td><td></td></tr><tr><td>EmbodiedGPT</td><td>CLIP-ViT</td><td>MPT-7B</td><td>0.04 / 0.36</td><td>0.27 / 0.53</td><td>0.13 / 0.38</td><td>0.00 / 0.40</td><td>0.10 / 0.40</td><td></td></tr><tr><td>Octopus (SFT Only)</td><td>CLIP-ViT</td><td>MPT-7B</td><td>0.11 / 0.33</td><td>0.27 / 0.47</td><td>0.16 / 0.38</td><td>0.13 / 0.33</td><td>0.15 / 0.37</td><td></td></tr><tr><td>Octopus (SFT + RLEF)</td><td>CLIP-ViT</td><td>MPT-7B</td><td>0.13 / 0.38</td><td>0.33 / 0.53</td><td>0.18 / 0.40</td><td>0.20 / 0.53</td><td>0.18 / 0.42</td><td></td></tr></table>",
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+    },
+    {
+        "type": "text",
+        "text": "metrics of goal task completion score to check whether the task is completed in the simulator and the plan score from human evaluation. We have 60 evaluation tasks, with 45 from the seen environment, and 15 that are unseen during training. We also have 45 routine tasks and 15 require reasoning. Please note that models like Octopus might not always accurately identify specific object names as they appear in the simulator (e.g., \"water_bottle_189\"). To address this, we implement a post-processing step for the generated code, substituting generic object references with their exact names from the simulator with simple string similarity matching. If multiple objects, we select the one closest to the agent.",
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "For Blind LLMs, we provide them with all the environment information in a textual format. Referring to Figure 3, we hope the Blind LLMs could perform as GPT-4 but internalize the system message. For TAPA utilizes the open-vocabulary detection (OVD) technique [65] to recognize objects within images and parse them into textual environmental messages, we still provide it with ground-truth environmental messages as an oracle setting.",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "CodeLLaMA Improves Coding but not Planning. The first two rows in Table 3 highlight the suboptimal task completion rate of the blind LLMs. Among them, CodeLLaMA boasts pre-training on a large programming dataset, resulting in a notable enhancement in code execution from our observation, with  $92\\%$  of the written code being successfully executed compared to LLaMA's  $24\\%$ . However, its prowess in planning remains limited. In contrast, the proposed Octopus MPT-7B model displays superior planning and task completion metrics while maintaining commendable coding abilities ( $72\\%$  of the written code can be executed). We surmise that the coding requirements within the OctoGibson environment might not be exceedingly intricate, rendering an advanced programming language model, like CodeLLaMA, less crucial, albeit beneficial. For more insight, although not shown in the table, our efforts to replace the MPT model with CodeLLaMA encountered challenges of generating non-sense outputs, suggesting that more refined code, or image-code paired data might be necessary for a successful Octopus-CodeLLaMA integration.",
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+    },
+    {
+        "type": "header",
+        "text": "Octopus: Embodied VLM from Environmental Feedback",
+        "bbox": [
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+        "page_idx": 10
+    },
+    {
+        "type": "page_number",
+        "text": "11",
+        "bbox": [
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+    },
+    {
+        "type": "image",
+        "img_path": "images/a68d0cb8ddc8c569626e79a3f1dea21e19a76649253c2fd8619d3bc8a989ce56.jpg",
+        "image_caption": [
+            "(a) TAPA (step) + CodeLLaMA"
+        ],
+        "image_footnote": [],
+        "bbox": [
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+        "page_idx": 11
+    },
+    {
+        "type": "image",
+        "img_path": "images/bf2d68a0a852df651c24935ae658d8b2430478c74c364fde7e43907caebd633c.jpg",
+        "image_caption": [
+            "(b) Octopus (SFT only)"
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+    },
+    {
+        "type": "image",
+        "img_path": "images/3c1e63dfccfb810c30e5665f109edf1a15ea91cbcda2b41185f1c6239dd2cad4.jpg",
+        "image_caption": [
+            "(c) Octopus (SFT + RLEF)"
+        ],
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+        "bbox": [
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+        "page_idx": 11
+    },
+    {
+        "type": "image",
+        "img_path": "images/f71f405984cf4e8393b9c1e753f545beaca6a3878d55a4d6b61507c55dadcd07.jpg",
+        "image_caption": [
+            "(d) Vision Input for Vision-Language Models (e.g., b, c, e)"
+        ],
+        "image_footnote": [],
+        "bbox": [
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+        ],
+        "page_idx": 11
+    },
+    {
+        "type": "image",
+        "img_path": "images/454c045c5ac0ea802e3826daf5c56f2251fb041a482a63198e9ed0dc3ce774fb.jpg",
+        "image_caption": [
+            "(e) GPT-4V Output",
+            "Fig. 5: Qualitative Results on the task of find a carboy in OctoGibson environment. We show that the models shown can write executable code, but the proposed Octopus has stronger planning ability, especially after RLEF. We also explore the performance of GPT-4V on the specific task."
+        ],
+        "image_footnote": [],
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "Blind LLMs Struggle with Extended Input Content. Our observations indicate that the step-level TAPA model, when supplied with a ground-truth object list, achieves a notable enhancement in planning. The primary distinction between it and the blind CodeLLaMA lies in the input length; the latter deals with protracted, pairwise relation content, complicating the language model's ability to extract crucial data from the environment message. This scenario highlights the inherent limitation of blind LLMs: relying on language alone to convey the entirety of environmental data can result in less informative input.",
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "Octopus Demonstrates Superior Task Generalization. Table 3 underscores Octopus's strong performance, evidencing its consistent edge over standalone language models in task completion. Its adeptness in adapting to previously unencumbered environments underlines the inherent advantages of vision-language models. A more detailed ablation analysis is provided later.",
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "RLEF Enhances Octopus's Planning Strategy. Table 3 shows Octopus's strong reasoning capabilities after the RLEF finetuning. An example can be observed in Fig. 5(b-c), where, after refinement via RLEF, Octopus astutely navigates to the cabinet housing the carboy instead of attempting a direct yet distant capture. Quantitatively, Octopus exhibits enhanced adaptability to previously unseen reasoning tasks, reinforcing its prowess in logical task resolution. When juxtaposed with other strategies, such as the embodied queries employed by EmbodiedGPT, RLEF emerges as the more efficacious approach.",
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+    },
+    {
+        "type": "page_number",
+        "text": "12",
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+    },
+    {
+        "type": "header",
+        "text": "J. Yang et al.",
+        "bbox": [
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+        ],
+        "page_idx": 11
+    },
+    {
+        "type": "image",
+        "img_path": "images/721900636f8ea71ee414022a79cb72aa5446b63aee809211ccdb846fe3fbce34.jpg",
+        "image_caption": [
+            "(a) On Tuning Different Components"
+        ],
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+        "bbox": [
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+        ],
+        "page_idx": 12
+    },
+    {
+        "type": "image",
+        "img_path": "images/57ce72ccfe3522c85a01290dbffdf88e7ffd2cd05813e6e487154389caab0f2d.jpg",
+        "image_caption": [
+            "(b) On SFT & RLEF and Model Size",
+            "(c) On Necessity of Vision Input",
+            "Fig.6: Ablation Study on model components, model size, and vision input. For bars with different colors, the upper bar denotes the number of successful reasoning tasks, and the lower is routine tasks."
+        ],
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+    {
+        "type": "text",
+        "text": "5.2 Ablation Study",
+        "text_level": 1,
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "Tunning Different Components Fig. 6 (a) demonstrates that solely adjusting the connector (marked \"fire\" in Fig. 4 (a)) leads to success for merely 4 out of 60 tasks. Conversely, finetuning both the connector and language decoder nudges the success rate slightly higher, with 5 tasks being accomplished.",
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+    {
+        "type": "text",
+        "text": "7B v.s. 3B Model Size We embarked on experiments centered on model size to discern the influence of the total parameter count on the efficacy of vision-language models. As illustrated in Fig. 6 (b), downsizing the model manifests in a noticeable performance drop. The congruency of results across both the SFT and RLEF models underscores the importance of an apt model size when sculpting vision-language models.",
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+    {
+        "type": "text",
+        "text": "Significance of Visual Inputs in Task Performance In our standard configuration, the vision component processes a sequence of image inputs, consisting of eight circularly captured first-person view (FPV) images, complemented by two bird's-eye view (BEV) images. With the intent to investigate the impact of visual inputs on task performance, we initiated an ablation study. In a modified setup, the sequence of these visual inputs was deliberately randomized, aiming to attenuate the strength of the visual signals. As illustrated in Fig. 6 (c), this intentional disruption in visual input consistency led to a pronounced decline in task performance. This result highlights the crucial role that clear and structured visual inputs play in the Octopus model, emphasizing that it significantly leverages visual cues for effective planning and task execution.",
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+    {
+        "type": "text",
+        "text": "5.3 Results on Minecraft and GTA Tasks",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "Results on OctoMC According to the OctoMC part in Sec. 3, we designed 40 tasks, each task is operated on 2 locations so a total of 80 tasks. We set aside 10 tasks as unseen tasks and 10 tasks as seen tasks, getting 60 training tasks and 30 testing tasks in OctoMC. Similar to OctoGibson, the training data for OctoMC is collected using GPT-4. The agent, guided by GPT-4, explores the Minecraft environment and generates action plans and corresponding code based on the provided system messages, environmental cues, and objectives.",
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+    {
+        "type": "text",
+        "text": "Table 4 shows that the SFT model trained on OctoMC can complete most tasks in both seen and unseen scenarios, demonstrating better performance compared to OctoGTA. However, upon analyzing the failure cases, we find that the",
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+    {
+        "type": "header",
+        "text": "Octopus: Embodied VLM from Environmental Feedback",
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+    },
+    {
+        "type": "page_number",
+        "text": "13",
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+    {
+        "type": "table",
+        "img_path": "images/42d0fb4a90c2701ba28200c150a89d74222db3bb3b5dea5d2adaeffc7b6c5aed.jpg",
+        "table_caption": [
+            "Table 4: Main Results for GTA and Minecraft Tasks. Despite limited training data, the Octopus still shows its good ability in task completion. In cells displaying two values, the first represents the task completion rate across the target validation task sets, while the second assesses the conceptual accuracy of the model's planning as judged by human evaluators."
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td rowspan=\"2\">Model</td><td rowspan=\"2\">Vision Model</td><td rowspan=\"2\">Language Model</td><td colspan=\"4\">OctoMC</td><td colspan=\"4\">OctoGTA</td></tr><tr><td>Seen Task</td><td>Unseen Task</td><td>All</td><td>Seen Task</td><td>Unseen Task</td><td>All</td><td></td><td></td></tr><tr><td>GPT-4</td><td>-</td><td>-</td><td>0.70 / 0.85</td><td>0.60 / 0.80</td><td>0.65 / 0.83</td><td>0.55 / 0.80</td><td>0.50 / 0.60</td><td>0.54 / 0.76</td><td></td><td></td></tr><tr><td>GPT-4V</td><td>-</td><td>-</td><td>0.75 / 0.85</td><td>0.70 / 0.85</td><td>0.73 / 0.85</td><td>0.58 / 0.85</td><td>0.50 / 0.70</td><td>0.56 / 0.82</td><td></td><td></td></tr><tr><td>EmbodiedGPT</td><td>CLIP-ViT</td><td>MPT-7B</td><td>0.30 / 0.55</td><td>0.20 / 0.60</td><td>0.25 / 0.58</td><td>0.15 / 0.38</td><td>0.20 / 0.60</td><td>0.16 / 0.42</td><td></td><td></td></tr><tr><td>Octopus (SFT Only)</td><td>CLIP-ViT</td><td>MPT-7B</td><td>0.30 / 0.60</td><td>0.20 / 0.70</td><td>0.25 / 0.65</td><td>0.18 / 0.48</td><td>0.20 / 0.60</td><td>0.18 / 0.50</td><td></td><td></td></tr><tr><td>Octopus (SFT + RLEF)</td><td>CLIP-ViT</td><td>MPT-7B</td><td>0.40 / 0.60</td><td>0.20 / 0.70</td><td>0.30 / 0.65</td><td>0.18 / 0.53</td><td>0.30 / 0.70</td><td>0.20 / 0.56</td><td></td><td></td></tr></table>",
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+    },
+    {
+        "type": "text",
+        "text": "model sometimes struggles with tasks requiring precise spatial reasoning. For instance, when tasked with killing a pig, the agent may have difficulty finding the creature with the exact angle and distance. While it shows that the agent relies on visual information to navigate and interact with the environment, it also means that even with correct planning, imprecise actions can lead to failure.",
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+    {
+        "type": "text",
+        "text": "Results on OctoGTA According to the OctoGTA part in Sec. 3, we designed 25 tasks. We set aside 5 tasks in the boat-related group (e.g., boat retrieval and shore return) as unseen tasks for testing only, using 2 different locations. We replicate the remaining 20 tasks for both training (8 different locations) and testing (2 locations). As a result, we have 160 training tasks and 50 testing tasks in OctoGTA. Unlike the training procedure for OctoGibson and OctoMC, the training data for OctoGTA is entirely created by the authors, as it is challenging to gather textual environmental messages in the GTA environment.",
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+    {
+        "type": "text",
+        "text": "Table 4 shows that, despite having only 160 training tasks, the SFT model can complete some tasks in both seen and unseen scenarios, and RLEF also outperforms. However, upon careful examination of the failure cases, we find that the model struggles with tasks that are not straightforward. For instance, when a wall separates the player from the car, the player still finds it difficult to decide to climb the wall, even if similar cases exist in the training data. Similar to OctoMC, as illustrated in Sec. 3, when approaching certain locations, the code involves functions like turnPlayer() and goForward() rather than a simple walkTo location). Consequently, even with correct planning, imprecise actual actions can still lead to task failure.",
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+        "type": "text",
+        "text": "6 Conclusion",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "This paper introduces Octopus, an embodied vision-language programmer designed to bridge the gap between high-level planning and real-world manipulation with programming. By open-sourcing our OctoVerse environments, dataset, and Octopus architecture, we aim to foster collaboration and innovation within the research community, paving the way for future developments in embodied vision-language programming.",
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+        "text": "14",
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+    {
+        "type": "header",
+        "text": "J. Yang et al.",
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+    {
+        "type": "text",
+        "text": "Acknowledgments and Disclosure of Funding",
+        "text_level": 1,
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+        "text": "This study is supported by the Ministry of Education, Singapore, under its MOE AcRF Tier 2 (MOET2EP20221- 0012), NTU NAP, and under the RIE2020 Industry Alignment Fund - Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from the industry partner(s).",
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+    {
+        "type": "text",
+        "text": "References",
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2024/Octopus_ Embodied Vision-Language Programmer from Environmental Feedback/e3940d59-4ec7-4868-b252-33e7cda42131_origin.pdf

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2024/Octopus_ Embodied Vision-Language Programmer from Environmental Feedback/full.md

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+# Octopus: Embodied Vision-Language Programmer from Environmental Feedback
+
+Jingkang Yang\*,1, Yuhao Dong\*,2,3, Shuai Liu\*,2,4, Bo Li\*,1, Ziyue Wang\*,1, Haoran Tan\*,4, Chencheng Jiang\*,5, Jiamu Kang\*,3, Yuanhan Zhang\*, Kaiyang Zhou\* and and Ziwei Liu\*
+
+$^{1}$  S-Lab, Nanyang Technological University  $^{2}$  Shanghai AI Laboratory  $^{3}$  Tsinghua University  $^{4}$  BUPT  $^{5}$  XJTU  $^{6}$  Hong Kong Baptist University  $\{\text{jingkang001, ziwei.liu}\}@\text{ntu.edu.sg}$
+
+![](images/6e1fffbae234e11e03cf9b934aa2f24e859d1db42aff5e8724d5471c83bc1950.jpg)  
+Fig. 1: Illustration of our vision-language programmer, Octopus, complete a task in GTA environment. Given a task in the form of natural language, Octopus relies on its egocentric vision to generate plans and the corresponding executable code.
+
+Abstract. Large vision-language models (VLMs) have achieved substantial progress in multimodal perception and reasoning. When integrated into an embodied agent, existing embodied VLM works either output detailed action sequences at the manipulation level or only provide plans at an abstract level, leaving a gap between high-level planning and real-world manipulation. To bridge this gap, we introduce Octopus, an embodied vision-language programmer that uses executable code generation as a medium to connect planning and manipulation. Octopus is designed to 1) proficiently comprehend an agent's visual and textual task objectives, 2) formulate intricate action sequences, and 3) generate executable code. To facilitate Octopus model development, we introduce OctoVerse: a suite of environments tailored for benchmarking
+
+vision-based code generators on a wide spectrum of tasks, ranging from mundane daily chores in simulators to sophisticated interactions in complex video games such as Grand Theft Auto (GTA) and Minecraft. To train Octopus, we leverage GPT-4 to control an explorative agent that generates training data, i.e., action blueprints and corresponding executable code. We also collect feedback that enables an enhanced training scheme called Reinforcement Learning with Environmental Feedback (RLEF). Through a series of experiments, we demonstrate Octopus's functionality and present compelling results, showing that the proposed RLEF refines the agent's decision-making. By open-sourcing our simulation environments, dataset, and model architecture, we aspire to ignite further innovation and foster collaborative applications within the broader embodied AI community. The project page is available at https://choiszt.github.io/0ctopus/.
+
+# 1 Introduction
+
+The rise of large language models (LLMs) [11,15,42,46,54] led to a surge in vision-language models (VLMs) [4,5,31,33,35-37], enabling tasks such as image/video-based descriptions [33], reasoning [14,38,60], and conversations [16,31]. In the realm of embodied AI, notable efforts [10,18,33] have trained agents to process visual input and relay motor control commands.
+
+Another approach to interacting with the environment focuses on task execution through code invocations, mirroring the human System-I stimulation [19,29] (automatic, intuitive actions) with predefined code, and leaving the System-II processes [19,29] (planning and reasoning) for large models. For example, referring to Fig. 1, planning a car ride with a pet might entail a subconscious checklist (e.g., getOutOf() the house, open() the car door), each action could be implemented using specific techniques [8,24] such as imitation learning [22,28]. This programmatic paradigm has been, although not in vision, leveraged by works [25, 50, 52, 53] using LLMs to craft programs and trigger APIs. Game-centric models like Voyager [55] have similarly employed GPT for function calls within game engines, though they often parse data directly from their environments.
+
+However, when incorporating visual perception, the programming paradigms are largely unexplored. Primary initiatives [48,58] can only output plans, which anchor their strategies only in initial environmental states or employ dynamic scene graphs for LLM inputs, respectively. Despite their innovations, the overreliance on pre-trained vision models to convert vision content into language can occasionally hinder the LLM's planning performance. The conversion from plans into real-world actions is still missing. While EmbodiedGPT [41] addresses the problem by integrating vision-language modeling for planning and then transitioning to manipulation using policy mapping, the capability of embodied vision-language models to generate executable programs remains largely uncharted.
+
+Our exploration aims to bridge this gap. An embodied vision-language programmer should integrate visual perspective with textual objectives to devise
+
+action plans and executable code (Fig. 1). However, existing simulator environments often lack the carefully designed functions necessary to support such models effectively. These functions should balance usefulness and complexity to avoid hindering the development of genuine embodied vision-language programmers. For example, the **explore_until()** function in Minecraft, which can lead the player directly to specific blocks without relying on vision information, may not be suitable for training these models.
+
+To meet the requirement, we carefully design and develop OctoVerse, a suite of environments consisting of diverse simulators, including (i) OctoGibson, built upon the photorealistic OmniGibson [32], (ii) OctoMC, developed on the infinitely creative, pixel-style Minecraft platform [2], and (iii) OctoGTA, adapted from the highly interactive and immersive Grand Theft Auto V (GTA-V) [1]. These environments enable the training and benchmarking of our embodied vision-language programming model in a wide range of scenarios, from daily household tasks to complex urban navigation and open-world exploration, while the function calls are tailored to be vision-dependent.
+
+Using the OctoVerse environment, we train Octopus by leveraging GPT-4 to collect data. We provide GPT-4 with system messages, environmental cues, and objectives, enabling it to formulate action strategies and code. Simultaneously, the agent captures visual perspectives, forming the image-code pair for Octopus training. During data collection, the agent receives simulator feedback, distinguishing successful moves from unsuccessful ones. We incorporate this feedback using Reinforcement Learning with Environmental Feedback (RLEF) and fine-tune Octopus using Proximal Policy Optimization (PPO) [51]. Empirically, Octopus demonstrates strong adaptability in various scenarios, outperforming existing models in task planning, code generation, and execution. The integration of RLEF further enhances Octopus's performance, showcasing the effectiveness of this training approach. In sum, our key contributions include:
+
+- A Novel Vision-Language Programming Benchmark: Three diverse embodied environments with designed tasks: (i) OctoGibson, which is developed upon OmniGibson [32], (ii) OctoMC that developed on Minecraft [2], and (iii) OctoGTA, which is adapted from GTA-V [1].  
+- A New Vision-Language Programming Model: An embodied vision-language planner and programmer trained with Reinforcement Learning with Environmental Feedback (RLEF), demonstrates compelling results.  
+- Insights on Vision-Language Programming: We extensively explore Octopus and share useful insights facilitating future research on visual planning and programming.
+
+# 2 Related Work
+
+# 2.1 Embodied AI Simulators
+
+Embodied AI has advanced significantly with the development of diverse simulation environments, enabling research tasks such as visual exploration [47],
+
+Table 1: Related Work for OctoVerse - Overview of Embodied AI Environments. We select three environments into OctoVerse and carefully design executable tasks and vision-dependent function calls (VC), in comparison to undesigned standard function calls (C).  
+
+<table><tr><td>Simulator</td><td>Kinematics</td><td>Continuous 
+Extended 
+States</td><td>Flexible 
+Materials</td><td>Deformable 
+Bodies</td><td>Realistic 
+Action 
+Execution</td><td>Game- or 
+World-Based</td><td>Formulated 
+Tasks</td><td>Function 
+Call 
+Type</td></tr><tr><td>OpenAIGym [9]</td><td>✓</td><td>×</td><td>×</td><td>×</td><td>✓</td><td>G</td><td>×</td><td>C</td></tr><tr><td>Matterport3D [12]</td><td>×</td><td>×</td><td>×</td><td>×</td><td>×</td><td>W</td><td>×</td><td>×</td></tr><tr><td>AI2THOR [30]</td><td>✓</td><td>×</td><td>×</td><td>×</td><td>×</td><td>G</td><td>×</td><td>C</td></tr><tr><td>VirtualHome [44]</td><td>×</td><td>×</td><td>×</td><td>×</td><td>×</td><td>G</td><td>×</td><td>×</td></tr><tr><td>House3D [57]</td><td>×</td><td>×</td><td>×</td><td>×</td><td>×</td><td>W</td><td>×</td><td>×</td></tr><tr><td>Habitat 1.0 [49]</td><td>✓</td><td>×</td><td>×</td><td>×</td><td>✓</td><td>W</td><td>×</td><td>C</td></tr><tr><td>Robosuite [66]</td><td>✓</td><td>×</td><td>×</td><td>×</td><td>✓</td><td>W</td><td>×</td><td>C</td></tr><tr><td>RFUniverse [21]</td><td>✓</td><td>×</td><td>✓</td><td>✓</td><td>✓</td><td>W</td><td>×</td><td>C</td></tr><tr><td>Minecraft [2]</td><td>✓</td><td>×</td><td>✓</td><td>×</td><td>✓</td><td>G</td><td>×</td><td>C</td></tr><tr><td>OctoMC</td><td>✓</td><td>×</td><td>✓</td><td>×</td><td>✓</td><td>G</td><td>✓</td><td>VC</td></tr><tr><td>GTA [1]</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>G</td><td>×</td><td>C</td></tr><tr><td>OctoGTA</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>G</td><td>✓</td><td>VC</td></tr><tr><td>OmniGibson [32]</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>W</td><td>×</td><td>C</td></tr><tr><td>OctoGibson</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>W</td><td>✓</td><td>VC</td></tr></table>
+
+navigation [56], and question-answering [17]. Several simulators, including AI2-THOR [30], VirtualHome [44], Habitat-Sim [49], SAPIEN [59], and Omnigibson [32], provide realistic representations of the world for investigating embodied AI challenges. OmniGibson [32] stands out for its high-fidelity simulation of diverse indoor and outdoor environments. OctoGibson environment further enhances OmniGibson with carefully designed function calls and formulated tasks, making it well-suited for vision-language programming.
+
+Game-related simulators like Arade [7], CHALET [61], and VRKitchen [23] also contribute significantly to embodied AI. Minecraft [2] has gained attention in reinforcement learning and game agents [6,20,39,55,63,64] but lacks the necessary structure for vision-language programming. OctoMC addresses this by providing designed function calls and formulated tasks. In contrast to Minecraft's voxel-based representations that limit transferability to real-world environments, GTA-V [1] offers a highly realistic environment. In this work, we introduce OctoGTA as a new setting, leveraging GTA-V's rich, open-world environment with incorporated tasks and function calls, extending this platform for embodied AI study.
+
+# 2.2 Embodied AI with Large Models
+
+The recent wave of research focuses on merging LLMs with embodied AI tasks [11, 42,46,54]. For instance, VoxPoser addresses robotic manipulation problems through unsupervised methods [27]. A group of projects, namely SayCan [3], Palm-E [18], RT-2 [10], and EmbodiedGPT [41], effectively integrate visual or linguistic cues with robot manipulation data. Outside the domain of robotic manipulation, initiatives like Voyager [55] and Smallville [43] harness the capabilities of GPT to interface with game functions, relying on preset functions to manage intricate manipulations. In a parallel vein, VisProg [25] leverages GPT-3 language prompts to craft Python programs, opening the door to a multitude of fascinating applications. While the proposed Octopus model also formulates plans
+
+Table 2: Related Work for Octopus - Overviewing Embodied AI Models. The proposed Octopus distinguishes itself from other models as a unified vision-language model for both plan and code generation.  
+
+<table><tr><td>Models</td><td>Release Date</td><td>Supported Environment</td><td>Vision Model</td><td>Code Generator</td><td>Action w/ Feedback</td><td>LLM Training Enabled</td></tr><tr><td>Text2Motion [34]</td><td>Mar. 2023</td><td>Sim</td><td>×</td><td>✓</td><td></td><td>×</td></tr><tr><td>Instruct2Act [26]</td><td>May 2023</td><td>Sim</td><td>×</td><td>✓</td><td></td><td>×</td></tr><tr><td>Lang2Rewards [62]</td><td>Jun. 2023</td><td>Sim</td><td>×</td><td>✓</td><td></td><td>×</td></tr><tr><td>VoxPoser [27]</td><td>Jul. 2023</td><td>Sim</td><td>✓</td><td>×</td><td></td><td>×</td></tr><tr><td>SayCan [3]</td><td>Apr. 2022</td><td>Real</td><td>✓</td><td>×</td><td></td><td>×</td></tr><tr><td>PALM-E [18]</td><td>Mar. 2023</td><td>Sim, Real</td><td>✓</td><td>×</td><td></td><td>✓</td></tr><tr><td>RT-2 [10]</td><td>Jul. 2023</td><td>Real</td><td>✓</td><td>×</td><td></td><td>✓</td></tr><tr><td>SayPlan [48]</td><td>Jun. 2023</td><td>Real</td><td>×</td><td>×</td><td></td><td>×</td></tr><tr><td>EmbodiedGPT [41]</td><td>May 2023</td><td>Sim</td><td>✓</td><td>×</td><td></td><td>✓</td></tr><tr><td>TaPA [58]</td><td>Jul. 2023</td><td>Sim</td><td>×</td><td>×</td><td></td><td>✓</td></tr><tr><td>Voyager [55]</td><td>May 2023</td><td>Game</td><td>×</td><td>✓</td><td></td><td>×</td></tr><tr><td>Steve-Eye [64]</td><td>Dec 2023</td><td>Game</td><td>✓</td><td>×</td><td></td><td>✓</td></tr><tr><td>RoboScript [13]</td><td>Feb 2024</td><td>Sim, Real</td><td>×</td><td>✓</td><td></td><td>×</td></tr><tr><td>Octopus</td><td>-</td><td>Sim, Game</td><td>✓</td><td>✓</td><td></td><td>✓</td></tr></table>
+
+and code, its distinguishing feature is the seamless integration of visual input in program and code generation. This also stands in contrast to other embodied planners like TAPA [58] and SayPlan [48], which deploy separate vision modules to translate visual data into linguistic inputs for LLMs. Octopus excels as a cohesive vision-language model, delivering not just plans but also code.
+
+More discussion on additional related works (Vision Language Model, and Feedback in Large Language Models) is included in the supplementary materials.
+
+# 3 The OctoVerse Environment
+
+In this section, we introduce three simulator environments designed to train and evaluate the Octopus model. For each environment, we will describe their overall information, the special design considerations that ensure the tasks are well-formulated and the callable functions are vision-dependent.
+
+OctoGibson We built the environment on the foundation of OmniGibson [32], an existing simulation framework that supports 1,000 daily activities across 50 scenes, featuring over 5,000 meticulously annotated objects. We incorporated 16 functions that the robot can execute, such as moveBot() and easyGrasp(). Within this environment, we meticulously crafted 476 tasks<sup>1</sup>, each with a well-defined initial state and a definitive termination state, allowing for a straightforward assessment of task completion. Among these tasks, 367 are routine tasks—simple and direct actions like "place a glass in a trash can," marked as Follow. Conversely, the remaining 109 are reasoning tasks that necessitate deeper comprehension. An example is "buy a chocolate," where the agent needs
+
+to know to pick a chocolate bar from the shelf and then place it, along with money, on the checkout counter, denoted as Reason tasks.
+
+To ensure our tasks are vision-aware, we deliberately constrain the usage of certain functions, such as moveBot(object), which moves the agent in front of the given object. To avoid making the task too easy and vision-agnostic, we limit the given parameter to a predefined set of large objects, such as tables and cupboards, rather than small items like cups and glasses. In this case, if the robot wants to pick up a cup, it needs to recognize whether the cup is on the table or in the cupboard. A simple moveBot(object) call with an inappropriate parameter would cause a runtime error. The full list of the functions is in the Appendix.
+
+OctoMC The OctoMC environment is built on Minecraft [2], a popular platform for reinforcement learning and game agents. We integrated 6 functional actions and crafted 40 tasks $^{2}$ , each designed to facilitate comprehensive observations and executions by the agent. These tasks are distributed across 10 different biomes, including indoor, outdoor, and underground settings, under varying weather conditions.
+
+However, existing Minecraft environments often lack vision-formulated tasks and the necessary structure for vision-language programming. For example, in Voyager [55], the exploreUntil() function allows the player to navigate directly to specific blocks without relying on vision information. This function works by randomly exploring the environment within a certain range until the desired object is found, at which point it returns a true value, enabling the agent to interact with the located object, such as gold blocks or trees. While effective, this approach is entirely automated and does not utilize visual information, making it unsuitable for our vision-based objectives.
+
+To address this limitation, we crafted a vision-dependent exploration function, teleport(yaw, distance), which operates within the robot's perceptual range. This function ensures that the agent's operations are vision-dependent and require active perception and navigation within the environment. For instance, to locate a tree block, the agent must actively navigate towards the direction of the forest, relying on visual cues to guide its exploration.
+
+OctoGTA The OctoGTA environment is built on GTA-V [1] with the help of the active GTA modding community. We integrated 19 functions and methodically crafted 25 tasks<sup>3</sup>, such as "help NPC drive their boat back to shore" and "mediate a fight between two NPCs," spanning across 5 groups (shown in Fig. 2 (b)). Each task is assigned to 5 different locations within the game world.
+
+We have implemented a set of functions that enable the agent to interact with the game world in a visually-aware manner. Similar to the design in Minecraft, we get rid of functions like walkTo location) that might trivialize the task of reaching a particular building or landmark. Instead, we provide functions such
+
+![](images/76f8463ebcdb002f43f190e5dcf9ce9a13839fdab317d9567151f0cc97fd2181.jpg)  
+Fig. 2: The Statistics of the OctoVerse Environment with Function Designs.
+
+as goForward(distance) and turnPlayer(degree) (Fig. 2 (b)). For tasks like mediating a fight between two NPCs, the essential function stopFight() only works when the player is within 5 meters of the fighting NPCs. These design choices ensure that the agent's operations are vision-dependent and require active perception and navigation within the environment.
+
+# 4 Octopus: The Embodied Vision-Language Programmer
+
+In this section, we present the procedure for training Octopus. Starting from collecting training data within the OctoVerse environment, Octopus builds upon a VLM architecture of Otter [31] and includes specialized RLEF modules to handle vision-language programming tasks. Fig. 4 illustrates the entire Octopus training pipeline.
+
+# 4.1 Training Data Collection
+
+We use the automatic training data collection pipeline described here for OctoGibson and OctoMC, with the latter using customized prompts inspired by Voyager [55]. For OctoGTA, we rely on human labor to hand-craft the training dataset due to the difficulty of obtaining textual environment messages in the GTA environment. In the following parts, we use OctoGibson as the primary example to illustrate the data collection pipeline. Note that the primary task in organizing training data is to form a succinct pairing: "vision input + current/historical states  $\rightarrow$  next step plan + executable code".
+
+Environment Info Collection As shown in Fig. 4 (a) and Fig. 3, we format an environment message for each state, encompassing attributes like Observed Objects, Observed Relations, Inventory, and more. Specifically, the simulator can provide us with an exact scene graph at each state, shaping the content for the first two parts. The inventory info is also accessible. The task, e.g., "cooking bacon" in Fig. 3, is represented by the Task Goal.
+
+Automation with GPT-4 After preparing the environment message, we crafted a structured system message to ensure that the robot not only understands its input but also maintains a consistent output format. A detailed
+
+![](images/6cdd976d7b1070035e63f9f0eb63fef5fd12684a6160ac0c712a8b05aaf85f52.jpg)  
+Fig.3: Data Collection Example for "Cook a Bacon" Task. GPT-4 perceives the environment through the environmental message and produces anticipated plans and code following the detailed system message. This code is subsequently executed in the simulator, directing the agent to the subsequent state. For each state, we gather the environmental message, wherein observed objects and relations are substituted by egocentric images to serve as the training input. The response from GPT-4 acts as the training output. Environmental feedback, specifically the determination of whether each target state is met, is documented for RLEF training.
+
+examination of this prompt can be found in the appendix. Experiments have shown that a well-articulated prompt enables GPT-4 to effectively generate executable code. It is important to note that the combined length of the system and environment messages can be extremely long, which may cause standard GPT-4 8K models to struggle with producing meaningful outputs. To address this issue, we employ the more robust GPT-4 32K model. As illustrated in Fig. 3, when GPT-4 receives a consistent system and environment message, it generates comprehensive outputs that include current scenario analysis, planning, and actionable code, supporting the training process in Section 4.3.
+
+Error Management Notably, GPT-4 collects training data under the main task of guiding the agent to complete tasks. However, GPT-4 is not infallible. Errors can manifest in multiple ways, ranging from syntax errors to physical challenges in the simulator. For instance, in Fig. 3, between states  $\#5$  and  $\#6$ , the action failed due to the long distance between the agent (holding bacon) and the pan. Such setbacks reset the task to its previous state. If a main task remains incomplete after 10 steps, it is deemed unsuccessful, and we terminate this task for budget concerns. However, all data pairs without syntax errors, regardless of the task's completion status, are valuable for refining instructions and improving the model's performance.
+
+Environmental Feedback GPT-4's continual trial-and-error approach while guiding the agent toward task completion serves a dual purpose: collecting vision-
+
+![](images/e7e886cf6a620a9c98069660b102aace976d3cf7ca9f6b7a9e3ddf03c6685308.jpg)  
+(a) Data Collection Pipeline
+
+![](images/d0ff0b0f34aadd20b48a71ac7fdbaec50a85b8bdbcf92072cdeb9a257db8ea84.jpg)  
+(b) Octopus Training Pipeline
+
+![](images/2b87a40202ac8da67bda6e6d35469e9fe011aa49fa4927b481cae1065f0d6289.jpg)  
+(c) Octopus (Otter model)
+
+![](images/41f47cd6d088c512b2b154383379f89ea6c4edf682b2cf99beebf2161af803f9.jpg)
+
+![](images/65b407683e8cb947939a9954f9b85688b2bf07e5c7b7867d83395650594fc61d.jpg)  
+Step 2: Training Reward Model
+
+![](images/d0606d05feeaff5577056bd7d0673252451a64569f06890ba05703f7fdf75430.jpg)  
+(d) RLEF Training  
+Fig. 4: How to train Octopus: data collection and training pipeline.
+
+output pairs and generating a rich set of feedback data. The automatic annotation of this feedback focuses on two levels: step-level and task-level judgments. Step-level judgment assesses the alignment of post-execution states with their target states. For instance, in Fig. 3, steps color-coded in green lead to positive feedback. One can visualize the action sequence for task completion as a tree, where each node indicates a step (subtask), encapsulating an action code. Accompanying each step is a binary value that denotes success or failure, giving preference to the successful branch over its counterpart. Task-level judgment, on the other hand, gauges the successful execution of the overall task. If the task is not completed as intended, every state within that task is labeled as negative, regardless of the status of the subtasks. This collated feedback data serves as a foundation for our Reinforcement Learning with Environmental Feedback (RLEF) methodology, which we discuss in greater detail in Section 4.4.
+
+# 4.2 Model Architecture
+
+The Octopus architecture (shown in Fig. 4 (c)) is heavily inspired by the Otter model [31], integrates the MPT-7B Language Decoder [40] and CLIP VIT-L/14 Vision Encoder [45]. Adopting design principles from Flamingo [4], Octopus employs the Perceiver Resampler and Cross-Gated Attention modules to enhance vision-language synergy. This architecture enables Octopus to excel in tasks requiring understanding of both visual and textual data. The Octopus is also compatible with other VLMs such as LLaVA [37].
+
+# 4.3 SFT: Supervised Finetuning with Instructions
+
+We train the Octopus model on our collected dataset from OctoVerse  $\mathcal{D}_{\mathrm{E}} = \{(\mathbf{X}_v,\mathbf{T}_i,\mathbf{T}_r)\}$  using token-level supervised fine-tuning (SFT) [42,54]. The Perceiver Resampler transforms images  $\mathbf{X}_v$  into visual tokens that condition subsequent layers via Cross-Gated Attention modules. The training objective is next-token prediction, modeling the likelihood of a targeted response  $\mathbf{T}_r$  as:
+
+$$
+p \left(\mathbf {T} _ {r} \mid \mathbf {T} _ {i}, \mathbf {X} _ {v}\right) = \prod_ {l = 1} ^ {L} p \left(t _ {l} \mid \mathbf {X} _ {v}, \mathbf {T} _ {i}, \mathbf {T} _ {r, <   l}\right). \tag {1}
+$$
+
+Note that  $\mathbf{T}_i$  denotes the instruction tokens and  $\mathbf{T}_{r, < l}$  denotes the response tokens before the current predicted token  $t_l$ . During inference, tokens are converted into natural language via the language decoder's text tokenizer.
+
+Visual observations  $\mathbf{X}_v = \{x_F^0,\dots ,x_F^7,x_B^0,x_B^1\}$  consist of 8 first-person view (FPV) images and two bird's-eye view (BEV) images for OctoGibson and OctoGTA. OctoMC only takes 4 FPV images. The FPV captures the agent's direct observations, while the BEV provides a holistic understanding of the environment. The eight FPV images are captured every 45 degrees, ensuring a complete 360-degree perspective.
+
+# 4.4 RLEF: Reinforcement Learning with Environmental Feedback
+
+In OctoVerse, task progression can be visualized as a tree (Fig. 4 (d)), where each node represents a sub-task with a binary value indicating success (1) or failure (0). If a node (or sub-task) has a value of 1, it is a step in the correct direction toward our end goal.
+
+Tree-based Task Representation According to the environmental feedback part in Sec. 4.1, environmental reward datasets  $\mathcal{D}_{\mathbb{R}} = (\mathbf{X}_v^*,\mathbf{T}_i^*,\mathbf{T}_r^i,\mathbf{T}_r^j,c)$  are organized, where  $\mathbf{T}_r^i$  and  $\mathbf{T}_r^j$  are responses sharing the same parent task  $\mathbf{T}_i^*$ , and  $c$  indicates the preferred response leading to task completion. This ensures the reward mechanism favors the successfully executed branch. Note that even if a parental node does not have multiple responses, we can still assign feedback according to the rule in Sec. 4.1.
+
+Reward Model Configuration A single-modal CodeLLaMA-7B model with an additional value head is fine-tuned on  $\mathcal{D}_{\mathrm{R}}$  as the reward model  $r_{\phi}$ . This text-based model assesses state transitions ( $\mathbf{T}_i^* \rightarrow \mathbf{T}_r^{i,j}$ ) to determine high-reward transitions, assisting the agent in task execution and completion. The rationale for using CodeLLaMA as the reward model is that evaluating rewards can be purely dependent on the textual output. Furthermore, CodeLLaMA's strong programming skills make it well-suited for assessing the quality and effectiveness of the generated code in the context of task completion.
+
+Policy Model Development The supervised fine-tuned model serves as the initial policy model  $\pi^{\mathrm{INIT}}$  with fixed parameters. A duplicate model,  $\pi_{\theta}^{\mathrm{RL}}$ , is initialized and trained using Proximal Policy Optimization (PPO) [51] to maximize response rewards. The loss function is:
+
+$$
+\mathcal {L} \left(\pi_ {\theta} ^ {\mathrm {R L}}\right) = - \mathbb {E} _ {\left(\mathbf {X} _ {v} ^ {*}, \mathbf {T} _ {i} ^ {*}\right) \in \mathcal {D} _ {\mathrm {R}}, \mathbf {T} _ {r} \sim \pi^ {\mathrm {R L}}} \left[ r _ {\phi} \left(\mathbf {T} _ {i} ^ {*}, \mathbf {T} _ {r}\right) - \beta \cdot \mathbb {D} _ {\mathrm {K L}} \left(\pi_ {\theta} ^ {\mathrm {R L}} \left(\mathbf {X} _ {v} ^ {*}, \mathbf {T} _ {i} ^ {*}\right) \| \pi^ {\mathrm {I N I T}} \left(\mathbf {X} _ {v} ^ {*}, \mathbf {T} _ {i} ^ {*}\right)\right) \right],
+$$
+
+where  $\beta$  acts as a hyper-parameter to regulate the magnitude of the Kullback-Leibler (KL) penalty.
+
+# 5 Experiments
+
+# 5.1 Main Results on OctoGibson
+
+Experimental Setup We first set up the OctoGibson to evaluate the performance of Octopus and other related models. Specifically, we are utilizing the
+
+Table 3: Main Results on OctoGibson. We compare various models: standalone language models, adapted vision-language planners, and our Octopus models, across different evaluation settings. In cells displaying two values, the first represents the task completion rate across the target validation task sets, while the second assesses the conceptual accuracy of the model's planning as judged by human evaluators. GT denotes that the model input is directly parsed from the simulator, with information on objects (O) or relations (R). Octopus shows consistent advance in task completion.  
+
+<table><tr><td rowspan="2">Model</td><td rowspan="2">Vision Model</td><td rowspan="2">Language Model</td><td colspan="6">Entire Goal Task</td></tr><tr><td>Seen Env</td><td>Unseen Env</td><td>Follow</td><td>Reason</td><td>All</td><td></td></tr><tr><td>GPT-4</td><td>-</td><td>-</td><td>0.42 / 0.69</td><td>0.46 / 0.67</td><td>0.49 / 0.78</td><td>0.27 / 0.40</td><td>0.43 / 0.68</td><td></td></tr><tr><td>GPT-4V</td><td>-</td><td>-</td><td>0.40 / 0.62</td><td>0.60 / 0.67</td><td>0.42 / 0.67</td><td>0.53 / 0.53</td><td>0.45 / 0.63</td><td></td></tr><tr><td>LLaMA</td><td>GT (O+R)</td><td>LLaMA2-7B</td><td>0.07 / 0.11</td><td>0.13 / 0.13</td><td>0.11 / 0.16</td><td>0.00 / 0.00</td><td>0.08 / 0.12</td><td></td></tr><tr><td>CodeLLaMA</td><td>GT (O+R)</td><td>CodeLLaMA-7B</td><td>0.09 / 0.20</td><td>0.20 / 0.40</td><td>0.16 / 0.31</td><td>0.00 / 0.07</td><td>0.12 / 0.25</td><td></td></tr><tr><td>TAPA (task-level)</td><td>QvD GT (O)</td><td>CodeLLaMA-7B</td><td>0.09 / 0.36</td><td>0.13 / 0.33</td><td>0.11 / 0.36</td><td>0.06 / 0.33</td><td>0.10 / 0.35</td><td></td></tr><tr><td>TAPA (step-level)</td><td>QvD GT (O)</td><td>CodeLLaMA-7B</td><td>0.16 / 0.42</td><td>0.13 / 0.27</td><td>0.18 / 0.38</td><td>0.07 / 0.40</td><td>0.15 / 0.38</td><td></td></tr><tr><td>EmbodiedGPT</td><td>CLIP-ViT</td><td>MPT-7B</td><td>0.04 / 0.36</td><td>0.27 / 0.53</td><td>0.13 / 0.38</td><td>0.00 / 0.40</td><td>0.10 / 0.40</td><td></td></tr><tr><td>Octopus (SFT Only)</td><td>CLIP-ViT</td><td>MPT-7B</td><td>0.11 / 0.33</td><td>0.27 / 0.47</td><td>0.16 / 0.38</td><td>0.13 / 0.33</td><td>0.15 / 0.37</td><td></td></tr><tr><td>Octopus (SFT + RLEF)</td><td>CLIP-ViT</td><td>MPT-7B</td><td>0.13 / 0.38</td><td>0.33 / 0.53</td><td>0.18 / 0.40</td><td>0.20 / 0.53</td><td>0.18 / 0.42</td><td></td></tr></table>
+
+metrics of goal task completion score to check whether the task is completed in the simulator and the plan score from human evaluation. We have 60 evaluation tasks, with 45 from the seen environment, and 15 that are unseen during training. We also have 45 routine tasks and 15 require reasoning. Please note that models like Octopus might not always accurately identify specific object names as they appear in the simulator (e.g., "water_bottle_189"). To address this, we implement a post-processing step for the generated code, substituting generic object references with their exact names from the simulator with simple string similarity matching. If multiple objects, we select the one closest to the agent.
+
+For Blind LLMs, we provide them with all the environment information in a textual format. Referring to Figure 3, we hope the Blind LLMs could perform as GPT-4 but internalize the system message. For TAPA utilizes the open-vocabulary detection (OVD) technique [65] to recognize objects within images and parse them into textual environmental messages, we still provide it with ground-truth environmental messages as an oracle setting.
+
+CodeLLaMA Improves Coding but not Planning. The first two rows in Table 3 highlight the suboptimal task completion rate of the blind LLMs. Among them, CodeLLaMA boasts pre-training on a large programming dataset, resulting in a notable enhancement in code execution from our observation, with  $92\%$  of the written code being successfully executed compared to LLaMA's  $24\%$ . However, its prowess in planning remains limited. In contrast, the proposed Octopus MPT-7B model displays superior planning and task completion metrics while maintaining commendable coding abilities ( $72\%$  of the written code can be executed). We surmise that the coding requirements within the OctoGibson environment might not be exceedingly intricate, rendering an advanced programming language model, like CodeLLaMA, less crucial, albeit beneficial. For more insight, although not shown in the table, our efforts to replace the MPT model with CodeLLaMA encountered challenges of generating non-sense outputs, suggesting that more refined code, or image-code paired data might be necessary for a successful Octopus-CodeLLaMA integration.
+
+![](images/a68d0cb8ddc8c569626e79a3f1dea21e19a76649253c2fd8619d3bc8a989ce56.jpg)  
+(a) TAPA (step) + CodeLLaMA
+
+![](images/bf2d68a0a852df651c24935ae658d8b2430478c74c364fde7e43907caebd633c.jpg)  
+(b) Octopus (SFT only)
+
+![](images/3c1e63dfccfb810c30e5665f109edf1a15ea91cbcda2b41185f1c6239dd2cad4.jpg)  
+(c) Octopus (SFT + RLEF)
+
+![](images/f71f405984cf4e8393b9c1e753f545beaca6a3878d55a4d6b61507c55dadcd07.jpg)  
+(d) Vision Input for Vision-Language Models (e.g., b, c, e)
+
+![](images/454c045c5ac0ea802e3826daf5c56f2251fb041a482a63198e9ed0dc3ce774fb.jpg)  
+(e) GPT-4V Output  
+Fig. 5: Qualitative Results on the task of find a carboy in OctoGibson environment. We show that the models shown can write executable code, but the proposed Octopus has stronger planning ability, especially after RLEF. We also explore the performance of GPT-4V on the specific task.
+
+Blind LLMs Struggle with Extended Input Content. Our observations indicate that the step-level TAPA model, when supplied with a ground-truth object list, achieves a notable enhancement in planning. The primary distinction between it and the blind CodeLLaMA lies in the input length; the latter deals with protracted, pairwise relation content, complicating the language model's ability to extract crucial data from the environment message. This scenario highlights the inherent limitation of blind LLMs: relying on language alone to convey the entirety of environmental data can result in less informative input.
+
+Octopus Demonstrates Superior Task Generalization. Table 3 underscores Octopus's strong performance, evidencing its consistent edge over standalone language models in task completion. Its adeptness in adapting to previously unencumbered environments underlines the inherent advantages of vision-language models. A more detailed ablation analysis is provided later.
+
+RLEF Enhances Octopus's Planning Strategy. Table 3 shows Octopus's strong reasoning capabilities after the RLEF finetuning. An example can be observed in Fig. 5(b-c), where, after refinement via RLEF, Octopus astutely navigates to the cabinet housing the carboy instead of attempting a direct yet distant capture. Quantitatively, Octopus exhibits enhanced adaptability to previously unseen reasoning tasks, reinforcing its prowess in logical task resolution. When juxtaposed with other strategies, such as the embodied queries employed by EmbodiedGPT, RLEF emerges as the more efficacious approach.
+
+![](images/721900636f8ea71ee414022a79cb72aa5446b63aee809211ccdb846fe3fbce34.jpg)  
+(a) On Tuning Different Components
+
+![](images/57ce72ccfe3522c85a01290dbffdf88e7ffd2cd05813e6e487154389caab0f2d.jpg)  
+(b) On SFT & RLEF and Model Size  
+(c) On Necessity of Vision Input  
+Fig.6: Ablation Study on model components, model size, and vision input. For bars with different colors, the upper bar denotes the number of successful reasoning tasks, and the lower is routine tasks.
+
+![](images/961aa896bd9576199f781b54b4eac79e45a7bc0e813ffd32b68085f9dc26eda2.jpg)
+
+# 5.2 Ablation Study
+
+Tunning Different Components Fig. 6 (a) demonstrates that solely adjusting the connector (marked "fire" in Fig. 4 (a)) leads to success for merely 4 out of 60 tasks. Conversely, finetuning both the connector and language decoder nudges the success rate slightly higher, with 5 tasks being accomplished.
+
+7B v.s. 3B Model Size We embarked on experiments centered on model size to discern the influence of the total parameter count on the efficacy of vision-language models. As illustrated in Fig. 6 (b), downsizing the model manifests in a noticeable performance drop. The congruency of results across both the SFT and RLEF models underscores the importance of an apt model size when sculpting vision-language models.
+
+Significance of Visual Inputs in Task Performance In our standard configuration, the vision component processes a sequence of image inputs, consisting of eight circularly captured first-person view (FPV) images, complemented by two bird's-eye view (BEV) images. With the intent to investigate the impact of visual inputs on task performance, we initiated an ablation study. In a modified setup, the sequence of these visual inputs was deliberately randomized, aiming to attenuate the strength of the visual signals. As illustrated in Fig. 6 (c), this intentional disruption in visual input consistency led to a pronounced decline in task performance. This result highlights the crucial role that clear and structured visual inputs play in the Octopus model, emphasizing that it significantly leverages visual cues for effective planning and task execution.
+
+# 5.3 Results on Minecraft and GTA Tasks
+
+Results on OctoMC According to the OctoMC part in Sec. 3, we designed 40 tasks, each task is operated on 2 locations so a total of 80 tasks. We set aside 10 tasks as unseen tasks and 10 tasks as seen tasks, getting 60 training tasks and 30 testing tasks in OctoMC. Similar to OctoGibson, the training data for OctoMC is collected using GPT-4. The agent, guided by GPT-4, explores the Minecraft environment and generates action plans and corresponding code based on the provided system messages, environmental cues, and objectives.
+
+Table 4 shows that the SFT model trained on OctoMC can complete most tasks in both seen and unseen scenarios, demonstrating better performance compared to OctoGTA. However, upon analyzing the failure cases, we find that the
+
+Table 4: Main Results for GTA and Minecraft Tasks. Despite limited training data, the Octopus still shows its good ability in task completion. In cells displaying two values, the first represents the task completion rate across the target validation task sets, while the second assesses the conceptual accuracy of the model's planning as judged by human evaluators.  
+
+<table><tr><td rowspan="2">Model</td><td rowspan="2">Vision Model</td><td rowspan="2">Language Model</td><td colspan="4">OctoMC</td><td colspan="4">OctoGTA</td></tr><tr><td>Seen Task</td><td>Unseen Task</td><td>All</td><td>Seen Task</td><td>Unseen Task</td><td>All</td><td></td><td></td></tr><tr><td>GPT-4</td><td>-</td><td>-</td><td>0.70 / 0.85</td><td>0.60 / 0.80</td><td>0.65 / 0.83</td><td>0.55 / 0.80</td><td>0.50 / 0.60</td><td>0.54 / 0.76</td><td></td><td></td></tr><tr><td>GPT-4V</td><td>-</td><td>-</td><td>0.75 / 0.85</td><td>0.70 / 0.85</td><td>0.73 / 0.85</td><td>0.58 / 0.85</td><td>0.50 / 0.70</td><td>0.56 / 0.82</td><td></td><td></td></tr><tr><td>EmbodiedGPT</td><td>CLIP-ViT</td><td>MPT-7B</td><td>0.30 / 0.55</td><td>0.20 / 0.60</td><td>0.25 / 0.58</td><td>0.15 / 0.38</td><td>0.20 / 0.60</td><td>0.16 / 0.42</td><td></td><td></td></tr><tr><td>Octopus (SFT Only)</td><td>CLIP-ViT</td><td>MPT-7B</td><td>0.30 / 0.60</td><td>0.20 / 0.70</td><td>0.25 / 0.65</td><td>0.18 / 0.48</td><td>0.20 / 0.60</td><td>0.18 / 0.50</td><td></td><td></td></tr><tr><td>Octopus (SFT + RLEF)</td><td>CLIP-ViT</td><td>MPT-7B</td><td>0.40 / 0.60</td><td>0.20 / 0.70</td><td>0.30 / 0.65</td><td>0.18 / 0.53</td><td>0.30 / 0.70</td><td>0.20 / 0.56</td><td></td><td></td></tr></table>
+
+model sometimes struggles with tasks requiring precise spatial reasoning. For instance, when tasked with killing a pig, the agent may have difficulty finding the creature with the exact angle and distance. While it shows that the agent relies on visual information to navigate and interact with the environment, it also means that even with correct planning, imprecise actions can lead to failure.
+
+Results on OctoGTA According to the OctoGTA part in Sec. 3, we designed 25 tasks. We set aside 5 tasks in the boat-related group (e.g., boat retrieval and shore return) as unseen tasks for testing only, using 2 different locations. We replicate the remaining 20 tasks for both training (8 different locations) and testing (2 locations). As a result, we have 160 training tasks and 50 testing tasks in OctoGTA. Unlike the training procedure for OctoGibson and OctoMC, the training data for OctoGTA is entirely created by the authors, as it is challenging to gather textual environmental messages in the GTA environment.
+
+Table 4 shows that, despite having only 160 training tasks, the SFT model can complete some tasks in both seen and unseen scenarios, and RLEF also outperforms. However, upon careful examination of the failure cases, we find that the model struggles with tasks that are not straightforward. For instance, when a wall separates the player from the car, the player still finds it difficult to decide to climb the wall, even if similar cases exist in the training data. Similar to OctoMC, as illustrated in Sec. 3, when approaching certain locations, the code involves functions like turnPlayer() and goForward() rather than a simple walkTo location). Consequently, even with correct planning, imprecise actual actions can still lead to task failure.
+
+# 6 Conclusion
+
+This paper introduces Octopus, an embodied vision-language programmer designed to bridge the gap between high-level planning and real-world manipulation with programming. By open-sourcing our OctoVerse environments, dataset, and Octopus architecture, we aim to foster collaboration and innovation within the research community, paving the way for future developments in embodied vision-language programming.
+
+# Acknowledgments and Disclosure of Funding
+
+This study is supported by the Ministry of Education, Singapore, under its MOE AcRF Tier 2 (MOET2EP20221- 0012), NTU NAP, and under the RIE2020 Industry Alignment Fund - Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from the industry partner(s).
+
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2024/Omni-Recon_ Harnessing Image-based Rendering for General-Purpose Neural Radiance Fields/0d53a505-587b-4461-974a-55e35a489a73_content_list.json

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+    {
+        "type": "text",
+        "text": "Omni-Recon: Harnessing Image-based Rendering for General-Purpose Neural Radiance Fields",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "Yonggan Fu, Huaizhi Qu, Zhifan Ye, Chaojian Li, Kevin Zhao, and Yingyan (Celine) Lin",
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+    {
+        "type": "text",
+        "text": "Georgia Institute of Technology {yfu314, zye327, cli851, kzhao14, celine.lin}@gatech.edu",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "Abstract. Recent breakthroughs in Neural Radiance Fields (NeRFs) have sparked significant demand for their integration into real-world 3D applications. However, the varied functionalities required by different 3D applications often necessitate diverse NeRF models with various pipelines, leading to tedious NeRF training for each target task and cumbersome trial-and-error experiments. Drawing inspiration from the generalization capability and adaptability of emerging foundation models, our work aims to develop one general-purpose NeRF for handling diverse 3D tasks. We achieve this by proposing a framework called Omni-Recon, which is capable of (1) generalizable 3D reconstruction and zero-shot multitask scene understanding, and (2) adaptability to diverse downstream 3D applications such as real-time rendering and scene editing. Our key insight is that an image-based rendering pipeline, with accurate geometry and appearance estimation, can lift 2D image features into their 3D counterparts, thus extending widely explored 2D tasks to the 3D world in a generalizable manner. Specifically, our Omni-Recon features a general-purpose NeRF model using image-based rendering with two decoupled branches: one complex transformer-based branch that progressively fuses geometry and appearance features for accurate geometry estimation, and one lightweight branch for predicting blending weights of source views. This design achieves state-of-the-art (SOTA) generalizable 3D surface reconstruction quality with blending weights reusable across diverse tasks for zero-shot multitask scene understanding. In addition, it can enable real-time rendering after baking the complex geometry branch into meshes, swift adaptation to achieve SOTA generalizable 3D understanding performance, and seamless integration with 2D diffusion models for text-guided 3D editing. Our code is available at: https://github.com/GATECH-EIC/Omni-Recon.",
+        "bbox": [
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+        "page_idx": 0
+    },
+    {
+        "type": "text",
+        "text": "1 Introduction",
+        "text_level": 1,
+        "bbox": [
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+            743,
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+            758
+        ],
+        "page_idx": 0
+    },
+    {
+        "type": "text",
+        "text": "Neural Radiance Fields (NeRFs) [41] have garnered significant attention and hold promise to become crucial enablers for emerging 3D applications. However, deploying NeRFs in real-world scenarios still requires substantial effort, as different 3D applications often demand distinct NeRF models with varied",
+        "bbox": [
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+        "page_idx": 0
+    },
+    {
+        "type": "text",
+        "text": "pipelines, leading to tedious NeRF training for each target task and cumbersome trial-and-error experiments. For instance, while both cross-scene generalization and real-time rendering are desirable in the real world, generalizable NeRFs for instant reconstruction [10,38,77] and real-time NeRFs using mesh-based rasterization [12,65,85] typically feature diverse pipelines, making it hard to simultaneously satisfy the two requirements. Additionally, understanding new scene properties [36,90,93] necessitates training new NeRF models, which is not scalable given the increasing number of scene properties of interest.",
+        "bbox": [
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+        "page_idx": 1
+    },
+    {
+        "type": "text",
+        "text": "In parallel, the emergence of foundation models has driven transformative progress in real-world artificial intelligence applications. Inspired by this exciting trend, there has been a growing interest in integrating NeRFs into this realm by developing general-purpose NeRFs for handling various 3D tasks. Specifically, similar to the generalization capability and adaptability of foundation models, we expect general-purpose NeRFs to possess three essential capabilities, including (1) generalizable 3D reconstruction for instant surface reconstruction and novel view synthesis, (2) zero-shot multitask scene understanding of various scene properties, e.g., semantics and edges, and (3) adaptability to diverse downstream tasks such as real-time rendering and scene editing. Achieving these capabilities is promising to enable users to effortlessly start from these general-purpose NeRFs when facing a new 3D application, significantly reducing the need for training and experimenting with various NeRF pipelines.",
+        "bbox": [
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+        "page_idx": 1
+    },
+    {
+        "type": "text",
+        "text": "Despite the promise, existing NeRF pipelines have not yet realized the mentioned capabilities. Firstly, per-scene optimized NeRFs [2, 3, 22, 25, 27, 42, 85] rely on costly per-scene training, thus precluding generalization capabilities. Secondly, although generalizable NeRF designs [10, 38, 54, 68, 75, 77, 82, 88] can achieve cross-scene generalization, their huge computational complexity limits their suitability for application scenarios where real-time rendering is crucial. Some generalizable surface reconstruction methods [34, 55] can support mesh extraction, but an expensive volumetric rendering process, independent of the extracted mesh, is still necessary for rendering novel views, leaving the demand for real-time rendering unmet. Furthermore, their potential for zero-shot scene understanding and scene editing remains unexplored.",
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+    {
+        "type": "text",
+        "text": "Given the significant potential and associated challenges, our work aims to advance the integration of NeRFs into the realm of foundation models by developing general-purpose NeRFs. To achieve this goal, we propose a framework called Omni-Recon, which is an image-based rendering pipeline that realizes all the mentioned capabilities. The key insight leveraged by our Omni-Recon is that an image-based rendering pipeline, when equipped with accurate geometry and appearance estimation, can lift 2D image features into their 3D counterparts, thus extending widely explored 2D tasks to the 3D world in a generalizable manner. Specifically, our contributions are summarized as follows:",
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+    {
+        "type": "header",
+        "text": "Y. Fu et al.",
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+    {
+        "type": "page_footnote",
+        "text": "- We introduce Omni-Recon, a general-purpose NeRF pipeline, which possesses both decent generalization capabilities, including generalizable 3D reconstruction and zero-shot multitask scene understanding, and adaptability to diverse downstream 3D applications.",
+        "bbox": [
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+    },
+    {
+        "type": "list",
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+        "list_items": [
+            "- Our Omni-Recon features a general-purpose NeRF model backbone using image-based rendering with two decoupled branches: one complex transformer-based geometry branch that progressively fuses geometry and appearance features to accurately predict a signed distance function (SDF), and one lightweight appearance branch for predicting blending weights of source views from the target scene. The advantages of this design include: (1) ensuring accurate geometry estimation for high-quality 3D reconstruction, and (2) enabling real-time rendering after baking the complex geometry branch into meshes and using the lightweight appearance branch as a Shader.",
+            "- We intriguingly find that a properly trained image-based rendering pipeline can directly lift 2D monocular priors to 3D novel views by reusing the blending weights predicted by the appearance branch and leverage this to enable generalizable, zero-shot, and multitask scene understanding.",
+            "- We demonstrate the adaptability of Omni-Recon by extending it to diverse downstream tasks, including: (1) a Nvdiffrast-based [31] real-time rendering pipeline using the extracted mesh, (2) parameter-efficient tuning (PET) of Omni-Recon on downstream generalizable scene understanding tasks, and (3) a new text-guided scene editing scheme by iteratively editing and reconstructing the source images from a 3D scene using 2D diffusion models.",
+            "- Extensive experiments demonstrate that our Omni-Recon can achieve state-of-the-art (SOTA) generalizable 3D surface reconstruction and scene understanding performance. Additionally, it can support real-time rendering of a new scene after rapid adaptation, along with easy-to-use 3D scene editing."
+        ],
+        "bbox": [
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+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "Our Omni-Recon underscores the potentially wide usage of image-based rendering pipelines in diverse real-world 3D applications, which may have been overlooked by previous works. We believe that the insights we provide could ignite future innovations in more advanced generalizable rendering pipelines.",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "2 Related Work",
+        "text_level": 1,
+        "bbox": [
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+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "Neural Radiance Fields. NeRFs [42], which represent the target scene as a continuous volume with density and view-dependent color, have garnered increasing attention thanks to their SOTA rendering quality when compared to prior approaches using explicit neural representations [24,39,60,66]. Subsequent works further improve NeRFs from various angles, such as enhancing their rendering quality [2,3,11,72], training NeRFs with sparse views [18,28,46,69,80,81], extending NeRFs to large-scale scenes [64,70,78,79,91], or applying NeRFs to other tasks, such as generative modeling [7,47,58,74], dynamic scenes [6,33,49, 50], or scene understanding [21,30,73,93].",
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "Generalizable NeRFs. Vanilla NeRFs rely on per-scene optimization, which limits their ability to perform instant reconstruction. To address this limitation, generalizable NeRF designs [10, 13, 29, 38, 54, 68, 75, 77, 82, 88] achieve cross-scene generalization by conditioning vanilla NeRFs on the source views from the target new scene. Specifically, [68, 75, 77, 88] take the extracted scene features from the source views as inputs to reconstruct the radiance field via a one-shot forward",
+        "bbox": [
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+        "page_idx": 2
+    },
+    {
+        "type": "header",
+        "text": "Omni-Recon",
+        "bbox": [
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+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "page_number",
+        "text": "3",
+        "bbox": [
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+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "pass. Subsequent works further improve cross-scene generalization capabilities by enhancing scene geometry estimation [10, 29, 38] and by introducing attention mechanisms and mixture-of-experts into NeRF backbone design [15, 36, 75]. Despite the promise of generalizable NeRFs, they still fall short in terms of rendering efficiency due to their huge computational complexity, which entails costly inference for each sampled point along the ray. While some generalizable surface reconstruction methods [34, 55] can support mesh extraction, a costly volumetric rendering process, independent of the extracted mesh, is still necessary for rendering novel views. This poses a hindrance to their deployment in 3D applications that require real-time rendering. Furthermore, their potential for zero-shot scene understanding and scene editing remains unexplored.",
+        "bbox": [
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+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "Efficient scene representations. Various scene representations have been proposed to accelerate NeRFs' training and inference process. To speed up NeRF training, [8,9,20,43,62] integrate explicit neural representations with volumetric rendering. In parallel, to speed up NeRF inference/rendering, [35,52,53] attempt to reduce the complexity of the multi-layer perceptron (MLP) model in NeRF. In addition, [22, 25, 27, 37, 53, 59, 87] explore the free space in a target 3D scene to improve the sampling efficiency and thus reduce unnecessary MLP inference. Motivated by the insight of deferred rendering [67], [12, 25, 65, 85] pre-compute scene features offline and thus require less computation at run-time. In particular, [12, 65, 85] represents NeRF as a set of textured polygons, where the textures can be pre-computed and thus the NeRF pipeline can be efficiently executed in modern graphics hardware. However, these efficient scene representations still necessitate costly per-scene optimization, lacking generalization capabilities.",
+        "bbox": [
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+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "3 Omni-Recon: The General-Purpose NeRF Backbone",
+        "text_level": 1,
+        "bbox": [
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+        "page_idx": 3
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+    {
+        "type": "text",
+        "text": "To develop general-purpose NeRFs for diverse 3D tasks, in this section, we design a general-purpose NeRF backbone and demonstrate its SOTA generalizable 3D reconstruction capability. We will leverage this backbone to enable zero-shot multitask scene understanding in Sec. 4 and adapt it to diverse downstream 3D tasks, including real-time rendering, PET for 3D scene understanding, and text-guided scene editing in Sec. 5.",
+        "bbox": [
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+        "page_idx": 3
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+    {
+        "type": "text",
+        "text": "3.1 Backbone Design Overview",
+        "text_level": 1,
+        "bbox": [
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+        "page_idx": 3
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+    {
+        "type": "text",
+        "text": "Design principles. Our design is driven by the following principles: (1) Similar to foundation models, the ability to instantly reconstruct a new scene with generalizable capabilities is essential. Therefore, we leverage image-based rendering [10,38,77], which lifts 2D image features into their 3D counterparts, as our backbone design and uncover their decent capabilities in diverse 3D applications, which may have been overlooked by previous works. (2) Considering a wide range of 3D applications that require real-time rendering, it is highly desirable that our backbone design can be seamlessly converted into efficient 3D representations, particularly meshes, to enable real-time rendering.",
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+    },
+    {
+        "type": "text",
+        "text": "Our backbone design. As shown in Fig. 1, the backbone model in OmniRecon leverages image-based rendering with two decoupled branches: one complex transformer-based geometry branch that progressively fuses geometry and",
+        "bbox": [
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+        "page_idx": 3
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+    {
+        "type": "page_number",
+        "text": "4",
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+    {
+        "type": "header",
+        "text": "Y. Fu et al.",
+        "bbox": [
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+    },
+    {
+        "type": "image",
+        "img_path": "images/5556299a659e0430e950b9d7ef37408a3da658d642f050271fb4e782aae15406.jpg",
+        "image_caption": [
+            "Fig. 1: An overview of our Omni-Recon framework."
+        ],
+        "image_footnote": [],
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "appearance features to accurately predict an SDF, and one lightweight appearance branch for estimating blending weights of source views. Specifically, the progressive feature fusion in the geometry branch empowers accurate geometry estimation for high-quality 3D reconstruction, and the decoupling between geometry and appearance enables real-time rendering after baking the complex geometry branch into meshes, with the lightweight appearance branch serving as a shader. Additionally, the nature of image-based rendering, when equipped with accurate geometry and appearance estimation, can enable a wide range of downstream 3D applications, as shown in Sec. 4 and Sec. 5.",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "3.2 The Overall Image-Based Rendering Pipeline",
+        "text_level": 1,
+        "bbox": [
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+        "page_idx": 4
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+    {
+        "type": "text",
+        "text": "Following generalizable NeRFs [10,38,77,88], our NeRF backbone is conditioned on source views from the target scene. This is achieved by estimating the geometry and appearance based on the extracted features of the source views from the target scene, which is elaborated as follows.",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "Acquire geometry features. Given  $N$  source views  $\\{I_i\\}_{i=1}^N \\in \\mathbb{R}^{H \\times W \\times 3}$  from the target scene, we utilize a CNN encoder [34, 55] to extract source features  $\\{\\mathbf{F}_i\\}_{i=1}^N \\in \\mathbb{R}^{H \\times W \\times C}$  from the source views. Next, we construct a 3D feature volume  $V \\in \\mathbb{R}^{M \\times M \\times M \\times C}$ , which serves similar roles as cost volumes for multiview stereo [10, 83], to aggregate geometry information from different source views, following [44, 55, 63]. Specifically, we divide the bounding volume of the scene into  $M^3$  voxels and project each voxel center onto  $N$  source views, where the mean and variance of  $N$  source features are computed and concatenated as the corresponding voxel feature. We then leverage a 3D U-Net [14] to further enhance the feature volume to acquire the final geometry feature volume  $V$ .",
+        "bbox": [
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+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "Acquire appearance features. For each sampled 3D point  $\\mathbf{p}(t) = \\mathbf{o} + t\\mathbf{d}$  along the ray emitted from the target pixel on the novel view with camera origin  $\\mathbf{o}$",
+        "bbox": [
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+    {
+        "type": "header",
+        "text": "Omni-Recon",
+        "bbox": [
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+        "page_idx": 4
+    },
+    {
+        "type": "page_number",
+        "text": "5",
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+        "page_idx": 4
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+    {
+        "type": "text",
+        "text": "and direction  $\\mathbf{d}$ , we project it on the source views to acquire the projected colors  $\\{\\mathbf{c}_i\\}_{i=1}^N$  and appearance features  $\\{\\mathbf{f}_i\\}_{i=1}^N$  using bilinear interpolation.",
+        "bbox": [
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+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "Geometry and appearance estimation. For more accurate reconstruction of the scene geometry and mitigation of the intrinsic color-density ambiguity [76], we estimate the per-point SDF and model the density as a function of SDF, following [40,55,76]. For appearance, we estimate the per-point radiance through color blending, i.e., predicting the blending weights of the projected colors on the source views to compose the per-point color, following [38,77,88]. The geometry and appearance are modeled using separate branches and can be formulated as:",
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+    {
+        "type": "equation",
+        "text": "\n$$\ns = \\mathbf {M} _ {s d f} (\\{\\mathbf {f} _ {i} \\} _ {i = 1} ^ {N}, V), \\quad \\{\\omega_ {i} \\} _ {i = 1} ^ {N} = \\mathbf {M} _ {c o l o r} (\\{\\mathbf {f} _ {i} \\} _ {i = 1} ^ {N}, \\mathbf {d}), \\qquad (1)\n$$\n",
+        "text_format": "latex",
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+    {
+        "type": "text",
+        "text": "where  $s$  is the SDF,  $\\{\\omega_i\\}_{i=1}^N$  are the blending weights, and  $\\mathbf{M}_{sdf}$  and  $\\mathbf{M}_{color}$  are the geometry and appearance branches, respectively. The per-point radiance can be derived as the weighted sum  $\\hat{\\mathbf{c}} = \\sum_{i=1}^{N} \\omega_i \\mathbf{c}_i$  across  $N$  source views.",
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+    {
+        "type": "text",
+        "text": "For SDF-based volumetric rendering, we adopt the formulation in NeuS [76]. Specifically, the color is accumulated along the  $K$  sampled points along each ray:",
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+    {
+        "type": "equation",
+        "text": "\n$$\n\\hat {\\mathbf {C}} = \\sum_ {j = 1} ^ {K} T _ {j} \\alpha_ {j} \\hat {\\mathbf {c}} _ {j}, \\quad \\alpha_ {j} = 1 - \\exp \\left(- \\int_ {t _ {j}} ^ {t _ {j + 1}} \\rho (t) d t\\right), \\tag {2}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
+            330,
+            390,
+            785,
+            430
+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "where  $T_{j} = \\prod_{n=1}^{j-1}(1 - \\alpha_{n})$  is the accumulated transmittance and  $\\rho(t)$  is the density determined by the estimated SDF, following the definition in NeuS [76]. Complexities of the two branches. When predicting the radiance of a sampled 3D point, the colors of valid projections on the source views are already close to the radiance of this 3D point. In light of this, we employ a lightweight  $\\mathbf{M}_{color}$ , consisting of three MLP layers, for efficient blending weight prediction. In contrast, the geometry branch should be allocated with sufficient complexity for accurately estimating the occlusion effects among different sampled points along the same ray as well as that among the sampled points and the source views. We introduce the design of the geometry branch in the next subsection.",
+        "bbox": [
+            212,
+            429,
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+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "3.3 The Proposed Transformer-based Geometry Branch",
+        "text_level": 1,
+        "bbox": [
+            215,
+            594,
+            689,
+            609
+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "Overview. Our geometry branch leverages transformer modules [71] to progressively fuse geometry and appearance features, thus properly handling the two occlusion effects mentioned above. Specifically, for the  $K$  sampled 3D points along the ray, we acquire their geometry features  $\\{\\mathbf{v}_k\\}_{k=1}^K$  from the geometry feature volume  $V$ , which are sequentially processed through  $B$  blocks ( $B = 2$  in our design), each consisting of a geometry transformer, an appearance transformer, and an occlusion transformer, to produce the per-point SDF.",
+        "bbox": [
+            212,
+            616,
+            787,
+            720
+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "The geometry transformer. The geometry transformer fuses the geometry features into its inputs by using  $\\{\\mathbf{v}_k\\}_{k=1}^K$  as the key and value in a cross-attention scheme [71] and performs attention across the sampled points along the same ray to model their occlusion effects. This process can be formulated as follows:",
+        "bbox": [
+            212,
+            722,
+            787,
+            782
+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\n\\mathbf {M} _ {s d f} ^ {g e o} (\\mathbf {x}, \\{\\mathbf {v} _ {k} \\} _ {k = 1} ^ {K}) = C r o s s A t t e n t i o n (\\mathbf {q} = \\mathbf {x}, \\mathbf {k} = \\mathbf {v} = \\{\\mathbf {v} _ {k} \\} _ {k = 1} ^ {K}), \\qquad (3)\n$$\n",
+        "text_format": "latex",
+        "bbox": [
+            274,
+            795,
+            785,
+            815
+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "page_number",
+        "text": "6",
+        "bbox": [
+            217,
+            114,
+            228,
+            126
+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "header",
+        "text": "Y. Fu et al.",
+        "bbox": [
+            271,
+            114,
+            349,
+            127
+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "where  $\\mathbf{x}$  is the input to the geometry transformer, which is exactly the geometry features themselves for the geometry transformer in the first block, and  $\\mathbf{q}$ ,  $\\mathbf{k}$ , and  $\\mathbf{v}$  are the query, key, and value of the attention scheme [71], respectively.",
+        "bbox": [
+            212,
+            146,
+            782,
+            191
+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "The appearance transformer. The appearance transformer integrates the appearance features  $\\{\\mathbf{f}_i\\}_{i=1}^N$  into the outputs of the geometry transformer, considering the potential occlusions among the sampled points and the source views that may cause invalid projections. This integration is achieved using subtraction attention [75,92], which is more effective for geometric relationship reasoning [75,92], with  $\\{\\mathbf{f}_i\\}_{i=1}^N$  as the key and value. Specifically, subtraction attention computes the attention scores between the input query features and each source view in the key features, which are then used to aggregate different source views in the value features into new output features with the same dimension as the input query features. This process can be formulated as follows:",
+        "bbox": [
+            212,
+            191,
+            784,
+            342
+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\n\\mathbf {M} _ {s d f} ^ {a p p r} (\\mathbf {x}, \\{\\mathbf {f} _ {i} \\} _ {i = 1} ^ {N}) = S u b A t t e n t i o n (\\mathbf {q} = \\mathbf {x}, \\mathbf {k} = \\mathbf {v} = \\{\\mathbf {f} _ {i} \\} _ {i = 1} ^ {N}), \\tag {4}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
+            287,
+            349,
+            784,
+            369
+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "where  $\\mathbf{x}$  is the output of  $\\mathbf{M}_{sdf}^{geo}$ . The detailed formulation of subtraction attention is provided in the supplementary materials.",
+        "bbox": [
+            212,
+            376,
+            782,
+            407
+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "The occlusion transformer. We further integrate an occlusion transformer that performs self-attention across sampled points along the same ray to model their occlusion effects explicitly:",
+        "bbox": [
+            212,
+            407,
+            782,
+            452
+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\n\\mathbf {M} _ {s d f} ^ {\\text {o c c}} (\\mathbf {x}) = \\text {S e l f A t t e n t i o n} (\\mathbf {q} = \\mathbf {k} = \\mathbf {v} = \\mathbf {x}), \\tag {5}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
+            346,
+            462,
+            784,
+            478
+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "where  $\\mathbf{x}$  is the output of  $\\mathbf{M}_{sdf}^{app}$ . We apply another MLP on top of the output features of the last  $M_{sdf}^{occ}$  module to predict the per-point SDF  $s$  in Eq. 1.",
+        "bbox": [
+            212,
+            486,
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+            518
+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "3.4 Training Objectives",
+        "text_level": 1,
+        "bbox": [
+            214,
+            530,
+            423,
+            546
+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "Our NeRF backbone is trained using two objectives: (1) a photometric loss  $\\mathcal{L}_{rgb}$  between the rendered images and the ground-truth images, and (2) a depth loss  $\\mathcal{L}_{depth}$  between the rendered depth  $\\hat{\\mathbf{D}} = \\sum_{j=1}^{K} T_j \\alpha_j t$  and the ground-truth depth. The overall objective can be formulated as follows:",
+        "bbox": [
+            212,
+            551,
+            782,
+            613
+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\n\\mathcal {L} = \\mathcal {L} _ {\\text {c o l o r}} + \\beta \\mathcal {L} _ {\\text {d e p t h}}, \\tag {6}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
+            424,
+            625,
+            784,
+            638
+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "where  $\\mathcal{L}_{rgb} = \\frac{1}{R}\\sum_{\\mathbf{r} = 1}^{R}\\left\\| \\hat{\\mathbf{C}}_{\\mathbf{r}} - \\mathbf{C}_{\\mathbf{r}}\\right\\|_{2}^{2}$  with  $R$  denoting the number of rendered pixels,  $\\mathcal{L}_{depth} = \\frac{1}{R}\\sum_{\\mathbf{r} = 1}^{R}\\left\\| \\hat{\\mathbf{D}}_{\\mathbf{r}} - \\mathbf{D}_{\\mathbf{r}}\\right\\|_{2}^{2}$ , and  $\\beta$  is set as 1.",
+        "bbox": [
+            212,
+            646,
+            782,
+            700
+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "3.5 Evaluation: Generalizable 3D Reconstruction",
+        "text_level": 1,
+        "bbox": [
+            214,
+            712,
+            633,
+            726
+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "Setup. We assess the backbone of Omni-Recon by evaluating its generalizable 3D reconstruction capability. Datasets: Following [10, 34, 40, 55], we adopt the DTU dataset [1], which comprises 124 different scenes and 7 distinct lighting conditions. For testing, 15 scenes are utilized, while the remaining scenes are allocated for training, following the dataset split in [34, 40, 55]. Training settings: During training, we employ  $N = 4$  source views with a resolution of  $640 \\times 512$ . The ray number per batch and batch size are set to 1024 and 2, respectively,",
+        "bbox": [
+            212,
+            733,
+            784,
+            839
+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "header",
+        "text": "Omni-Recon",
+        "bbox": [
+            645,
+            114,
+            730,
+            126
+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "page_number",
+        "text": "7",
+        "bbox": [
+            774,
+            114,
+            785,
+            126
+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "table",
+        "img_path": "images/392c5243bf0bcf854f9c5821518b5bda36908a739af619ada6a432a7161008d9.jpg",
+        "table_caption": [
+            "Table 1: Benchmark the quantitative performance in sparse view mesh reconstruction in terms of Chamfer distance (the lower, the better) on 15 testing scenes on DTU. The best scores are in bold, and the second-best are underlined."
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Method</td><td>Mean</td><td>24</td><td>37</td><td>40</td><td>55</td><td>63</td><td>65</td><td>69</td><td>83</td><td>97</td><td>105</td><td>106</td><td>110</td><td>114</td><td>118</td><td>122</td></tr><tr><td>COLMAP [57]</td><td>1.52</td><td>0.90</td><td>2.89</td><td>1.63</td><td>1.08</td><td>2.18</td><td>1.94</td><td>1.61</td><td>1.30</td><td>2.34</td><td>1.28</td><td>1.10</td><td>1.42</td><td>0.76</td><td>1.17</td><td>1.14</td></tr><tr><td>MVSNet [83]</td><td>1.22</td><td>1.05</td><td>2.52</td><td>1.71</td><td>1.04</td><td>1.45</td><td>1.52</td><td>0.88</td><td>1.29</td><td>1.38</td><td>1.05</td><td>0.91</td><td>0.66</td><td>0.61</td><td>1.08</td><td>1.16</td></tr><tr><td>IDR [86]</td><td>3.39</td><td>4.01</td><td>6.40</td><td>3.52</td><td>1.91</td><td>3.96</td><td>2.36</td><td>4.85</td><td>1.62</td><td>6.37</td><td>5.97</td><td>1.23</td><td>4.73</td><td>0.91</td><td>1.72</td><td>1.26</td></tr><tr><td>VolSDF [84]</td><td>3.41</td><td>4.03</td><td>4.21</td><td>6.12</td><td>0.91</td><td>8.24</td><td>1.73</td><td>2.74</td><td>1.82</td><td>5.14</td><td>3.09</td><td>2.08</td><td>4.81</td><td>0.60</td><td>3.51</td><td>2.18</td></tr><tr><td>UNISURF [48]</td><td>4.39</td><td>5.08</td><td>7.18</td><td>3.96</td><td>5.30</td><td>4.61</td><td>2.24</td><td>3.94</td><td>3.14</td><td>5.63</td><td>3.40</td><td>5.09</td><td>6.38</td><td>2.98</td><td>4.05</td><td>2.81</td></tr><tr><td>NeuS [76]</td><td>4.00</td><td>4.57</td><td>4.49</td><td>3.97</td><td>4.32</td><td>4.63</td><td>1.95</td><td>4.68</td><td>3.83</td><td>4.15</td><td>2.50</td><td>1.52</td><td>6.47</td><td>1.26</td><td>5.57</td><td>6.11</td></tr><tr><td>PixelNeRF [88]</td><td>6.18</td><td>5.13</td><td>8.07</td><td>5.85</td><td>4.40</td><td>7.11</td><td>4.64</td><td>5.68</td><td>6.76</td><td>9.05</td><td>6.11</td><td>3.95</td><td>5.92</td><td>6.26</td><td>6.89</td><td>6.93</td></tr><tr><td>IBRNet [77]</td><td>2.32</td><td>2.29</td><td>3.70</td><td>2.66</td><td>1.83</td><td>3.02</td><td>2.83</td><td>1.77</td><td>2.28</td><td>2.73</td><td>1.96</td><td>1.87</td><td>2.13</td><td>1.58</td><td>2.05</td><td>2.09</td></tr><tr><td>MVSNeRF [10]</td><td>2.09</td><td>1.96</td><td>3.27</td><td>2.54</td><td>1.93</td><td>2.57</td><td>2.71</td><td>1.82</td><td>1.72</td><td>2.29</td><td>1.75</td><td>1.72</td><td>1.47</td><td>1.29</td><td>2.09</td><td>2.26</td></tr><tr><td>SparseNeuS [40]</td><td>1.96</td><td>2.17</td><td>3.29</td><td>2.74</td><td>1.67</td><td>2.69</td><td>2.42</td><td>1.58</td><td>1.86</td><td>1.94</td><td>1.35</td><td>1.50</td><td>1.45</td><td>0.98</td><td>1.86</td><td>1.87</td></tr><tr><td>VolRecon [55]</td><td>1.38</td><td>1.20</td><td>2.59</td><td>1.56</td><td>1.08</td><td>1.43</td><td>1.92</td><td>1.11</td><td>1.48</td><td>1.42</td><td>1.05</td><td>1.19</td><td>1.38</td><td>0.74</td><td>1.23</td><td>1.27</td></tr><tr><td>ReTR [34]</td><td>1.17</td><td>1.05</td><td>2.31</td><td>1.44</td><td>0.98</td><td>1.18</td><td>1.52</td><td>0.88</td><td>1.35</td><td>1.30</td><td>0.87</td><td>1.07</td><td>0.77</td><td>0.59</td><td>1.05</td><td>1.12</td></tr><tr><td>Omni-Recon (Ours)</td><td>1.13</td><td>0.91</td><td>2.13</td><td>1.52</td><td>0.93</td><td>1.09</td><td>1.70</td><td>0.84</td><td>1.29</td><td>1.20</td><td>0.83</td><td>1.04</td><td>0.81</td><td>0.55</td><td>1.05</td><td>1.05</td></tr></table>",
+        "bbox": [
+            223,
+            186,
+            776,
+            358
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "following [34,55]. Mesh extraction: We utilize TSDF fusion [45,63] to reconstruct scene meshes from predicted source view depths, following [34,55]. More detailed model/training settings can be found in the supplementary materials.",
+        "bbox": [
+            212,
+            366,
+            784,
+            411
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "Baselines. We primarily benchmark against SOTA generalizable neural implicit reconstruction methods [16, 34, 55], along with generalizable neural rendering methods [10, 77, 88], per-scene optimization methods [48, 76, 84, 86], and multiview stereo methods [57, 83]. We directly report their official results in [34, 55].",
+        "bbox": [
+            212,
+            412,
+            784,
+            472
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "Sparse view reconstruction. We benchmark the performance of sparse view reconstruction using three views from each testing scene in DTU, following the setting in [16, 34, 55]. As shown in Tab. 1, we can observe that (1) Our Omni-Recon can achieve new SOTA reconstruction performance, with the lowest Chamfer distance averaged over all scenes. Additionally, as visualized in Fig. 2 (Row 1), our reconstructed mesh can exhibit smooth surfaces and correctly maintain the structure, e.g., the hole in the mouth. (2) Our method achieves the best reconstruction performance in 10 out of 15 scenes, indicating its general effectiveness across diverse scenes.",
+        "bbox": [
+            212,
+            474,
+            787,
+            609
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "Generalization to more diverse scenes. To demonstrate the generalization capability on more diverse scenes with larger domain shifts compared to the training scenes, we further benchmark the mesh reconstruction quality on the NeRF-Synthetic dataset [41] and NSVF-Synthetic [37] under a full-view reconstruction setting, i.e., the depth of each source view from each scene's training set is estimated and fused into a mesh using TSDF fusion [45, 63]. As visualized in Fig. 2 (Rows 2-4), we observe that our Omni-Recon can deliver notably higher-quality meshes with better-maintained structures, fewer holes, and smoother surfaces compared to the strongest baselines ReTR [34] and VolRecon [55]. The decent generalization capability of our method is attributed to the proper decoupling and fusion of geometry and appearance features in our backbone. More visualizations and ablation studies, such as the contributions of each backbone component, are provided in the supplementary materials.",
+        "bbox": [
+            212,
+            611,
+            787,
+            808
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "Rendering quality. We further benchmark the performance of generalizable novel view rendering on the test scenes from DTU. As shown in Tab. 2, our",
+        "bbox": [
+            212,
+            809,
+            785,
+            839
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "page_number",
+        "text": "8",
+        "bbox": [
+            217,
+            114,
+            228,
+            126
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "header",
+        "text": "Y. Fu et al.",
+        "bbox": [
+            271,
+            114,
+            349,
+            127
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "image",
+        "img_path": "images/253e7b23dc30b8c24932e32989d56897591d241547df22e0ec08fea8de4aceb3.jpg",
+        "image_caption": [
+            "Fig. 2: Visualize the reconstructed mesh of our Omni-Recon and the two strongest baselines [34, 55]. Row 1: Scan65 from the test scenes of DTU; Rows 2-4: Scenes from NeRF-Synthetic [41] and NSVF-Synthetic [37], which present relatively challenging cases due to domain shifts w.r.t. the training scenes from DTU."
+        ],
+        "image_footnote": [],
+        "bbox": [
+            222,
+            143,
+            759,
+            452
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "table",
+        "img_path": "images/9ff302180515419a10fe20e0b50cdca2ecaeee10f37c1cfedf4bacf84920425f.jpg",
+        "table_caption": [
+            "Table 2: Benchmark the quality of novel view rendering in terms of PSNR on DTU."
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Method</td><td>Mean.</td><td>24</td><td>37</td><td>40</td><td>55</td><td>63</td><td>65</td><td>69</td><td>83</td><td>97</td><td>105</td><td>106</td><td>110</td><td>114</td><td>118</td><td>122</td></tr><tr><td>VolRecon [55]</td><td>24.58</td><td>22.33</td><td>20.59</td><td>21.53</td><td>23.72</td><td>24.2</td><td>23.65</td><td>24.47</td><td>22.77</td><td>23.54</td><td>22.62</td><td>26.89</td><td>27.44</td><td>25.76</td><td>30.14</td><td>29.19</td></tr><tr><td>ReTR [34]</td><td>25.59</td><td>24.32</td><td>21.84</td><td>23.4</td><td>24.56</td><td>26.31</td><td>24.5</td><td>24.63</td><td>24.3</td><td>24.58</td><td>23.85</td><td>27.84</td><td>27.97</td><td>26.76</td><td>30.03</td><td>28.96</td></tr><tr><td>Omni-Recon</td><td>26.32</td><td>24.77</td><td>22.33</td><td>23.92</td><td>25.56</td><td>26.37</td><td>24.75</td><td>25.19</td><td>24.94</td><td>24.92</td><td>25.06</td><td>28.39</td><td>28.63</td><td>28.01</td><td>31.49</td><td>30.5</td></tr></table>",
+        "bbox": [
+            222,
+            529,
+            777,
+            580
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "method achieves the highest PSNR, e.g., a  $+0.73$  and  $+1.74$  PSNR improvement over ReTR and VolRecon, respectively. As will be demonstrated in Sec. 5.4, this PSNR can be notably boosted with super-fast Nvdiffrast-based finetuning [31].",
+        "bbox": [
+            212,
+            590,
+            787,
+            638
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "4 Omni-Recon: Enable Zero-shot Scene Understanding",
+        "text_level": 1,
+        "bbox": [
+            212,
+            652,
+            763,
+            671
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "To enable the understanding of various 3D scene properties, one approach is to jointly train the NeRF backbone on all target tasks. However, this approach is not scalable due to the diversity of scene properties, such as semantic segmentation, edge detection, and keypoint prediction, making it costly to cover new properties of interest. Therefore, it is highly desirable to enable zero-shot scene understanding for new tasks that were not seen during training.",
+        "bbox": [
+            212,
+            676,
+            787,
+            768
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "4.1 Our Strategy: The Predict-then-Blend Strategy",
+        "text_level": 1,
+        "bbox": [
+            212,
+            773,
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+    {
+        "type": "text",
+        "text": "Our hypothesis. We hypothesize that with accurate geometry and appearance estimation, the blending weights initially learned for radiance can be repurposed for other scene understanding tasks. In essence, just as blending source colors",
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+        "page_idx": 8
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+    {
+        "type": "header",
+        "text": "Omni-Recon",
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+    {
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+        "text": "9",
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+    {
+        "type": "image",
+        "img_path": "images/2499968a06e76450b64793bc71a72b1a6d2e791f72f49d34b6b6f2244b7a3f6b.jpg",
+        "image_caption": [
+            "Fig. 3: Benchmark our predict-then-blend strategy with the render-then-predict baseline across different scene understanding tasks on Replica [61] and ScanNet [17]."
+        ],
+        "image_footnote": [],
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "yields radiance for each sampled 3D point, blending the properties of source views using the same weights can yield other properties. This hypothesis stems from the observation that scene appearance is closely related to other scene properties and thus regions with similar appearance are likely to share similar scene properties, such as semantics and edges. As such, the blending weights learned for appearance can also indicate the weighting for other scene properties.",
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+    {
+        "type": "text",
+        "text": "Our method. Motivated by this hypothesis, our Omni-Recon employs a predict-then-blend strategy to enable zero-shot multitask scene understanding by lifting 2D monocular priors to 3D novel views. Specifically, considering the prevalence of 2D monocular vision models, our method first utilizes pretrained 2D models to generate predictions (e.g., logits in semantic segmentation) on each source view, denoted as  $\\{\\mathbf{P}_i\\}_{i=1}^N$ . Then, the property of each sampled point can be obtained by blending their projected predictions on source views  $\\{\\mathbf{p}_i\\}_{i=1}^N$ , reusing the RGB blending weights, i.e.,  $\\hat{\\mathbf{p}} = \\sum_{i=1}^{N} \\omega_i \\mathbf{p}_i$ . The final pixel-wise prediction can be derived using volumetric rendering similar to RGB reconstruction in Eq. 2.",
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+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "4.2 Evaluation: Zero-shot Multitask Scene Understanding",
+        "text_level": 1,
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "Setup. To evaluate the zero-shot scene understanding capabilities of our model, we adopt two indoor 3D scene datasets: Replica [61] and ScanNet [17], following",
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+    {
+        "type": "page_number",
+        "text": "10",
+        "bbox": [
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+        "page_idx": 9
+    },
+    {
+        "type": "header",
+        "text": "Y. Fu et al.",
+        "bbox": [
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+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "the dataset splits in [93] and [36], respectively. Specifically, we focus on four scene understanding tasks: semantic segmentation, edge detection, and two keypoint detection tasks defined in [89]. For these tasks, the 2D monocular priors are provided by [51], [5], and [56], respectively. We employ mIoU for the semantic segmentation task and  $\\ell_1$  error for the other three tasks as default metrics.",
+        "bbox": [
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+        "page_idx": 10
+    },
+    {
+        "type": "text",
+        "text": "Baseline. An intuitive way to achieve zero-shot 3D scene understanding is to leverage existing 2D monocular models to predict the scene properties on top of the rendered images, a process referred to as a render-then-predict pipeline. We benchmark this strategy against our proposed predict-then-blend strategy.",
+        "bbox": [
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+        ],
+        "page_idx": 10
+    },
+    {
+        "type": "text",
+        "text": "Zero-shot scene understanding benchmark. As shown in Tab. 3 and Fig. 3, we observe that (1) Our predict-then-blend strategy achieves decent scene understanding performance on unseen scenes and unseen tasks, which echoes our hypothesis in Sec. 4.1 that 2D monocular priors can be elevated to 3D novel views through",
+        "bbox": [
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+        "page_idx": 10
+    },
+    {
+        "type": "table",
+        "img_path": "images/a5a803a16789e034a88f4166eb0abbf59d457244b624b5dce79c7549634176b2.jpg",
+        "table_caption": [
+            "Table 3: Benchmark the two zero-shot scene understanding strategies across tasks."
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Dataset</td><td>Method</td><td>↑ Semantic</td><td>↓ Edge</td><td>↓ Key Point</td><td>↓ Key Point 3D</td></tr><tr><td rowspan=\"2\">Replica [61]</td><td>Render-then -Predict</td><td>15.64</td><td>0.0456</td><td>0.1101</td><td>0.0470</td></tr><tr><td>Predict-then -Blend (Ours)</td><td>32.11</td><td>0.0412</td><td>0.0774</td><td>0.0176</td></tr><tr><td rowspan=\"2\">ScanNet [17]</td><td>Render-then -Predict</td><td>41.32</td><td>0.0471</td><td>0.0568</td><td>0.0412</td></tr><tr><td>Predict-then -Blend (Ours)</td><td>61.11</td><td>0.0434</td><td>0.0424</td><td>0.0197</td></tr></table>",
+        "bbox": [
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+        "page_idx": 10
+    },
+    {
+        "type": "text",
+        "text": "blending weight reuse, thanks to the high correlation between scene appearance and other properties; (2) Compared to the render-then-predict scheme, our method achieves notably higher quantitative accuracy, e.g., a  $19.79\\%$  higher mIoU on ScanNet, and consistently better visual effects across all tasks. We attribute this improvement to two aspects: (1) Our predict-then-blend strategy can avoid feeding noisy inputs caused by rendering errors to the 2D monocular models, thus reducing reliance on the robustness of monocular models and ensuring more accurate monocular priors; (2) The multiview information gathered by our strategy can contribute to a more accurate understanding of objects with limited observations under a monocular setting. More experiments on other vision models like CLIP-LSeg [32] are in the supplementary materials.",
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "5 Omni-Recon: Adaptability to Downstream 3D Tasks",
+        "text_level": 1,
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "In addition to the generalization capabilities, we further demonstrate the adaptability of our Omni-Recon by extending it to three different types of downstream 3D applications. These demonstrations highlight the potentially broad utility of image-based rendering pipelines, which may have been overlooked by previous works, and can be generalized to a wider range of real-world 3D applications.",
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "5.1 Application 1: A New Real-time Rendering Pipeline",
+        "text_level": 1,
+        "bbox": [
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+        "page_idx": 10
+    },
+    {
+        "type": "text",
+        "text": "To rapidly enable real-time rendering using Omni-Recon's pretrained backbone, we build a real-time rendering pipeline based on Nvdiffrast [31]. This involves baking the complex geometry branch into meshes using TSDF fusion [45,63] as discussed in Sec. 3.5, and using the lightweight appearance branch as a Shader.",
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+        "page_idx": 10
+    },
+    {
+        "type": "text",
+        "text": "Our real-time rendering pipeline. As illustrated in Fig. 1 (bottom left), for any novel view of a new scene, we first transform its camera pose to clip space as required by Nvdiffrast [31]. Then, we perform a rasterization process to obtain the intersection point of each camera ray with the extracted mesh. Next, these",
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+        "page_idx": 10
+    },
+    {
+        "type": "header",
+        "text": "Omni-Recon",
+        "bbox": [
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+        "page_idx": 10
+    },
+    {
+        "type": "page_number",
+        "text": "11",
+        "bbox": [
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+        "page_idx": 10
+    },
+    {
+        "type": "text",
+        "text": "intersection points are input into the shader to predict the color by blending their projected colors on the source views as described in Sec. 3.2. Thanks to the blending scheme, where valid projections can provide strong appearance clues, the rendering quality is often satisfactory even without finetuning.",
+        "bbox": [
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+        ],
+        "page_idx": 11
+    },
+    {
+        "type": "text",
+        "text": "Joint mesh and Shader finetuning. To further enhance the rendering quality, we can jointly finetune the mesh and the Shader in a gradient-based manner using differentiable rendering enabled by Nvdiffrast [31]. Specifically, we finetune the location of each vertex in the mesh and the parameters of the lightweight Shader. Additionally, we periodically prune redundant mesh faces that cannot intersect with any camera rays in one training epoch. More details are provided in the supplementary materials.",
+        "bbox": [
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+        "page_idx": 11
+    },
+    {
+        "type": "text",
+        "text": "5.2 Application 2: Parameter-Efficient Tuning for 3D Understanding",
+        "text_level": 1,
+        "bbox": [
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+        "page_idx": 11
+    },
+    {
+        "type": "text",
+        "text": "We explore the possibility of finetuning Omni-Recon for various downstream 3D tasks, utilizing a series of 3D scene understanding tasks as demonstrations. Specifically, we conduct PET [19] on top of Omni-Recon's pretrained backbone by incorporating low-rank adapters [26] into the linear layers within the transformer modules introduced in Sec. 3.3, and only tuning these adapters while keeping the pretrained backbone frozen. It is worth noting that when evaluating the performance of the tuned model, we assess it on new scenes that have never been encountered during both training and tuning, representing a generalizable scene understanding scenario [36].",
+        "bbox": [
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+        "page_idx": 11
+    },
+    {
+        "type": "text",
+        "text": "5.3 Application 3: Text-guided 3D Scene Editing",
+        "text_level": 1,
+        "bbox": [
+            214,
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+        "page_idx": 11
+    },
+    {
+        "type": "text",
+        "text": "We target text-guided 3D scene editing, which involves editing a 3D scene based on textual instructions. To achieve this, our Omni-Recon capitalizes on the nature of image-based rendering, allowing edits made to 2D source views to be propagated to 3D novel views. Specifically, leveraging the availability of powerful 2D diffusion models, we use publicly available ones to edit the 2D source views. However, doing so naively may result in inconsistent source views, as the same textual instruction may lead to different visualizations, thus degrading reconstruction quality. Inspired by [23], which iteratively edits the training images and retransmits a new NeRF to achieve scene editing, we address the inconsistent editing issue by proposing an iterative editing and reconstruction pipeline.",
+        "bbox": [
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+        "page_idx": 11
+    },
+    {
+        "type": "text",
+        "text": "The iterative editing and reconstruction pipeline. To ensure consistent 3D editing, we iterate between source view editing and reconstruction as follows: First, we edit each source view using the diffusion model InstructPix2Pix [4], which is guided by both textual instructions and the original image to maintain image fidelity. While the edited source views can faithfully follow the textual instructions, they may exhibit inconsistencies in the edited region. Next, we reconstruct each source view by conditioning on their nearby source views, as described in Sec. 3.2. This process improves the 3D consistency among source views at the expense of reduced image fidelity and less adherence to instructions. We then iterate through the editing and reconstruction steps to progressively enhance image fidelity, 3D consistency, and adherence to instructions. The resulting source views are finally used to reconstruct the entire scene.",
+        "bbox": [
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+        "page_idx": 11
+    },
+    {
+        "type": "page_number",
+        "text": "12",
+        "bbox": [
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+        "page_idx": 11
+    },
+    {
+        "type": "header",
+        "text": "Y. Fu et al.",
+        "bbox": [
+            271,
+            114,
+            349,
+            127
+        ],
+        "page_idx": 11
+    },
+    {
+        "type": "table",
+        "img_path": "images/7959c899f57713872cbe4f7112f394692f483946af3bdf56dfa96c6ffd985863.jpg",
+        "table_caption": [
+            "Table 4: Benchmark the rendering speed and rendering quality of our real-time rendering pipeline and the baselines [34,55] on DTU. The best PSNR is in bold, and the shortest finetuning time that our pipelines surpass the strongest baseline is underlined."
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Method</td><td>FPS</td><td>Mean</td><td>24</td><td>37</td><td>40</td><td>55</td><td>63</td><td>65</td><td>69</td><td>83</td><td>97</td><td>105</td><td>106</td><td>110</td><td>114</td><td>118</td><td>122</td></tr><tr><td>VolRecon [55]</td><td>0.029</td><td>24.58</td><td>22.33</td><td>20.59</td><td>21.53</td><td>23.72</td><td>24.2</td><td>23.65</td><td>24.47</td><td>22.77</td><td>23.54</td><td>22.62</td><td>26.89</td><td>27.44</td><td>25.76</td><td>30.14</td><td>29.19</td></tr><tr><td>ReTR [34]</td><td>0.024</td><td>25.59</td><td>24.32</td><td>21.84</td><td>23.4</td><td>24.56</td><td>26.31</td><td>24.5</td><td>24.63</td><td>24.3</td><td>24.58</td><td>23.85</td><td>27.84</td><td>27.97</td><td>26.76</td><td>30.03</td><td>28.96</td></tr><tr><td>Ours w/o ft.</td><td rowspan=\"7\">71.3(40.82)</td><td>22.96</td><td>20.12</td><td>19.71</td><td>22.27</td><td>22.78</td><td>24.55</td><td>21.77</td><td>21.51</td><td>26.72</td><td>22.33</td><td>24.49</td><td>22.52</td><td>22.93</td><td>24.68</td><td>23.84</td><td>24.12</td></tr><tr><td>Ours (ft. 10s)</td><td>25.68</td><td>21.42</td><td>21.68</td><td>24.06</td><td>24.12</td><td>28.19</td><td>24.10</td><td>23.95</td><td>31.65</td><td>24.41</td><td>28.15</td><td>25.63</td><td>25.85</td><td>26.15</td><td>26.82</td><td>26.89</td></tr><tr><td>Ours (ft. 20s)</td><td>27.21</td><td>22.63</td><td>22.92</td><td>25.12</td><td>25.42</td><td>30.03</td><td>25.95</td><td>26.16</td><td>33.19</td><td>26.22</td><td>30.36</td><td>27.44</td><td>27.04</td><td>26.93</td><td>29.15</td><td>29.65</td></tr><tr><td>Ours (ft. 30s)</td><td>27.78</td><td>23.2</td><td>23.26</td><td>25.54</td><td>25.7</td><td>30.59</td><td>26.83</td><td>26.96</td><td>33.66</td><td>26.47</td><td>30.73</td><td>28.14</td><td>27.70</td><td>27.1</td><td>30.17</td><td>30.65</td></tr><tr><td>Ours (ft. 1min)</td><td>28.34</td><td>24.69</td><td>24.11</td><td>25.76</td><td>26.05</td><td>30.93</td><td>27.66</td><td>27.49</td><td>33.68</td><td>27.07</td><td>30.97</td><td>28.51</td><td>28.54</td><td>27.35</td><td>31.17</td><td>31.54</td></tr><tr><td>Ours (ft. 3min)</td><td>28.95</td><td>25.2</td><td>24.32</td><td>25.94</td><td>26.16</td><td>32.15</td><td>28.99</td><td>27.88</td><td>34.94</td><td>27.35</td><td>31.62</td><td>28.93</td><td>28.97</td><td>27.56</td><td>31.70</td><td>32.49</td></tr><tr><td>Ours (ft. 5min)</td><td>29.02</td><td>25.34</td><td>24.36</td><td>25.63</td><td>26.21</td><td>32.16</td><td>29.33</td><td>27.81</td><td>34.94</td><td>27.32</td><td>31.74</td><td>29.04</td><td>29.05</td><td>27.69</td><td>31.74</td><td>32.89</td></tr></table>",
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+    },
+    {
+        "type": "text",
+        "text": "5.4 Evaluation: Effectiveness on Different Downstream 3D Tasks",
+        "text_level": 1,
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "Real-time rendering. We evaluate our real-time rendering pipeline in Sec. 5.1 in terms of both rendering quality and rendering speed, measured on an NVIDIA A5000 GPU, using the DTU dataset. Specifically, we finetune the scene meshes, extracted by TSDF fusion as mentioned in Sec. 3.5, and the Shader in no more than 5 minutes and record the rendering quality at different time steps.",
+        "bbox": [
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+        ],
+        "page_idx": 12
+    },
+    {
+        "type": "text",
+        "text": "Observations and analysis: As shown in Tab. 4, we can observe that (1) Without any finetuning, the rendering pipeline already exhibits certain rendering capabilities, despite the model backbone being trained using volumetric rendering with more than one sampled point; (2) Our rendering pipeline can be swiftly finetuned to significantly boost the PSNR. For instance, with just a 10-second finetuning, our rendering pipeline can match the average rendering quality of the strongest baseline, ReTR [34]; With 1-minute/2-minute finetuning, it can surpass ReTR by a +2.75 and +3.36 PSNR improvement on average, respectively; (3) Our rendering pipeline enables real-time rendering with 71.3 FPS, i.e., a >2458× speed-up over the baselines. Here, the assumption is that the feature extraction of each source view from a target scene can be done in a one-time effort and does not account for throughput. If we still consider this feature extraction latency for each rendering, the resulting 40.82 FPS still meets the real-time requirement.",
+        "bbox": [
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+        ],
+        "page_idx": 12
+    },
+    {
+        "type": "text",
+        "text": "These experiments indicate that given a new scene, our Omni-Recon is promising to enable both instant and accurate reconstruction and real-time rendering.",
+        "bbox": [
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+        ],
+        "page_idx": 12
+    },
+    {
+        "type": "text",
+        "text": "PET for 3D Understanding. To evaluate the adaptability of our Omni-Recon, we apply PET to it for various scene understanding tasks on ScanNet [17]. Specifically, we finetune a LoRA adaptor [26] for each task on a set of training scenes and evaluate on",
+        "bbox": [
+            214,
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+        ],
+        "page_idx": 12
+    },
+    {
+        "type": "table",
+        "img_path": "images/c63928ecbcfdd75abcb62989ff79d2000c5ab9e24cabcd3753af41f87d89e7bb.jpg",
+        "table_caption": [
+            "Table 5: Evaluate the effectiveness of PET on Omni-Recon and benchmark with SRay [36]."
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Method</td><td>↑ Sem. mIoU</td><td>↑ Sem. Total Acc</td><td>↑ Sem. Avg Acc</td><td>↓ Edge</td><td>↓ Key Point</td><td>↓ Key Point 3D</td></tr><tr><td>SRay [36]</td><td>57.15</td><td>78.24</td><td>62.55</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Ours (Zero-shot)</td><td>61.11</td><td>80.80</td><td>69.17</td><td>0.0434</td><td>0.0424</td><td>0.0197</td></tr><tr><td>Ours (PET + Zero-shot)</td><td>62.35</td><td>81.84</td><td>69.89</td><td>0.0342</td><td>0.0207</td><td>0.0132</td></tr></table>",
+        "bbox": [
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+        ],
+        "page_idx": 12
+    },
+    {
+        "type": "text",
+        "text": "non-overlapping test scenes, representing a generalizable 3D understanding setting, following the dataset split described in [36]. We benchmark against the SOTA generalizable semantic segmentation work, SRay [36].",
+        "bbox": [
+            212,
+            750,
+            784,
+            794
+        ],
+        "page_idx": 12
+    },
+    {
+        "type": "text",
+        "text": "Observations and analysis: As shown in Tab. 5, we can observe that (1) After PET, our model outperforms SRay in terms of generalizable semantic segmentation with a  $+5.20\\%$  mIoU and a  $+7.34\\%$  accuracy improvement; (2) Despite",
+        "bbox": [
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+            842
+        ],
+        "page_idx": 12
+    },
+    {
+        "type": "header",
+        "text": "Omni-Recon",
+        "bbox": [
+            645,
+            114,
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+            126
+        ],
+        "page_idx": 12
+    },
+    {
+        "type": "page_number",
+        "text": "13",
+        "bbox": [
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+        "page_idx": 12
+    },
+    {
+        "type": "image",
+        "img_path": "images/6c7be7d29fa88ab9cad67e961ec0abd8829608f9dfec4b17ab9ee1cd19f8a98c.jpg",
+        "image_caption": [
+            "Fig. 4: Visualize the scene understanding performance on hard cases before/after PET."
+        ],
+        "image_footnote": [],
+        "bbox": [
+            272,
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+    {
+        "type": "image",
+        "img_path": "images/33fe245c26fdbe0ab18443afadf59a49d34d5e8ccb4aa221368daa5744f241c1.jpg",
+        "image_caption": [
+            "Fig. 5: Three text-guided scene editing examples using our pipeline in Sec. 5.3."
+        ],
+        "image_footnote": [],
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+    {
+        "type": "text",
+        "text": "having limited trainable parameters, PET can enhance the scene understanding performance across tasks, especially on challenging cases visualized in Fig. 4, indicating the adaptability of Omni-Recon to diverse downstream tasks.",
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+    {
+        "type": "text",
+        "text": "Text-guided scene editing. We visualize the edited examples in Fig. 5, showcasing the ability to ensure both instruction-following and 3D consistency.",
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+    {
+        "type": "text",
+        "text": "6 Conclusion",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "Motivated by the significant demand for supporting various functionalities required by different 3D applications through a unified NeRF model, our work aims to develop a general-purpose NeRF capable of handling a broad spectrum of 3D tasks. We propose a framework called Omni-Recon, which features a general-purpose NeRF model using image-based rendering with two separate branches, and demonstrate its integration with various 3D tasks. Specifically, our Omni-Recon can achieve SOTA generalizable 3D reconstruction quality, enable zero-shot multitask scene understanding, achieve SOTA scene understanding performance and real-time rendering after rapid adaptation, and support scene editing. Omni-Recon underscores the potential of image-based rendering pipelines in diverse real-world 3D applications, which may have been underestimated by prior studies, thus sparking advancements in future rendering pipelines.",
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+        "type": "page_number",
+        "text": "14",
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+    {
+        "type": "header",
+        "text": "Y. Fu et al.",
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+    {
+        "type": "text",
+        "text": "Acknowledgement",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "The work is supported by the National Science Foundation (NSF) through the SCH program (Award number: 1838873) and CoCoSys, one of the seven centers in JUMP 2.0, a Semiconductor Research Corporation (SRC) program sponsored by DARPA.",
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+    {
+        "type": "text",
+        "text": "References",
+        "text_level": 1,
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+        "page_idx": 18
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2024/Omni-Recon_ Harnessing Image-based Rendering for General-Purpose Neural Radiance Fields/0d53a505-587b-4461-974a-55e35a489a73_origin.pdf

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2024/Omni-Recon_ Harnessing Image-based Rendering for General-Purpose Neural Radiance Fields/full.md

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+# Omni-Recon: Harnessing Image-based Rendering for General-Purpose Neural Radiance Fields
+
+Yonggan Fu, Huaizhi Qu, Zhifan Ye, Chaojian Li, Kevin Zhao, and Yingyan (Celine) Lin
+
+Georgia Institute of Technology {yfu314, zye327, cli851, kzhao14, celine.lin}@gatech.edu
+
+Abstract. Recent breakthroughs in Neural Radiance Fields (NeRFs) have sparked significant demand for their integration into real-world 3D applications. However, the varied functionalities required by different 3D applications often necessitate diverse NeRF models with various pipelines, leading to tedious NeRF training for each target task and cumbersome trial-and-error experiments. Drawing inspiration from the generalization capability and adaptability of emerging foundation models, our work aims to develop one general-purpose NeRF for handling diverse 3D tasks. We achieve this by proposing a framework called Omni-Recon, which is capable of (1) generalizable 3D reconstruction and zero-shot multitask scene understanding, and (2) adaptability to diverse downstream 3D applications such as real-time rendering and scene editing. Our key insight is that an image-based rendering pipeline, with accurate geometry and appearance estimation, can lift 2D image features into their 3D counterparts, thus extending widely explored 2D tasks to the 3D world in a generalizable manner. Specifically, our Omni-Recon features a general-purpose NeRF model using image-based rendering with two decoupled branches: one complex transformer-based branch that progressively fuses geometry and appearance features for accurate geometry estimation, and one lightweight branch for predicting blending weights of source views. This design achieves state-of-the-art (SOTA) generalizable 3D surface reconstruction quality with blending weights reusable across diverse tasks for zero-shot multitask scene understanding. In addition, it can enable real-time rendering after baking the complex geometry branch into meshes, swift adaptation to achieve SOTA generalizable 3D understanding performance, and seamless integration with 2D diffusion models for text-guided 3D editing. Our code is available at: https://github.com/GATECH-EIC/Omni-Recon.
+
+# 1 Introduction
+
+Neural Radiance Fields (NeRFs) [41] have garnered significant attention and hold promise to become crucial enablers for emerging 3D applications. However, deploying NeRFs in real-world scenarios still requires substantial effort, as different 3D applications often demand distinct NeRF models with varied
+
+pipelines, leading to tedious NeRF training for each target task and cumbersome trial-and-error experiments. For instance, while both cross-scene generalization and real-time rendering are desirable in the real world, generalizable NeRFs for instant reconstruction [10,38,77] and real-time NeRFs using mesh-based rasterization [12,65,85] typically feature diverse pipelines, making it hard to simultaneously satisfy the two requirements. Additionally, understanding new scene properties [36,90,93] necessitates training new NeRF models, which is not scalable given the increasing number of scene properties of interest.
+
+In parallel, the emergence of foundation models has driven transformative progress in real-world artificial intelligence applications. Inspired by this exciting trend, there has been a growing interest in integrating NeRFs into this realm by developing general-purpose NeRFs for handling various 3D tasks. Specifically, similar to the generalization capability and adaptability of foundation models, we expect general-purpose NeRFs to possess three essential capabilities, including (1) generalizable 3D reconstruction for instant surface reconstruction and novel view synthesis, (2) zero-shot multitask scene understanding of various scene properties, e.g., semantics and edges, and (3) adaptability to diverse downstream tasks such as real-time rendering and scene editing. Achieving these capabilities is promising to enable users to effortlessly start from these general-purpose NeRFs when facing a new 3D application, significantly reducing the need for training and experimenting with various NeRF pipelines.
+
+Despite the promise, existing NeRF pipelines have not yet realized the mentioned capabilities. Firstly, per-scene optimized NeRFs [2, 3, 22, 25, 27, 42, 85] rely on costly per-scene training, thus precluding generalization capabilities. Secondly, although generalizable NeRF designs [10, 38, 54, 68, 75, 77, 82, 88] can achieve cross-scene generalization, their huge computational complexity limits their suitability for application scenarios where real-time rendering is crucial. Some generalizable surface reconstruction methods [34, 55] can support mesh extraction, but an expensive volumetric rendering process, independent of the extracted mesh, is still necessary for rendering novel views, leaving the demand for real-time rendering unmet. Furthermore, their potential for zero-shot scene understanding and scene editing remains unexplored.
+
+Given the significant potential and associated challenges, our work aims to advance the integration of NeRFs into the realm of foundation models by developing general-purpose NeRFs. To achieve this goal, we propose a framework called Omni-Recon, which is an image-based rendering pipeline that realizes all the mentioned capabilities. The key insight leveraged by our Omni-Recon is that an image-based rendering pipeline, when equipped with accurate geometry and appearance estimation, can lift 2D image features into their 3D counterparts, thus extending widely explored 2D tasks to the 3D world in a generalizable manner. Specifically, our contributions are summarized as follows:
+
+- Our Omni-Recon features a general-purpose NeRF model backbone using image-based rendering with two decoupled branches: one complex transformer-based geometry branch that progressively fuses geometry and appearance features to accurately predict a signed distance function (SDF), and one lightweight appearance branch for predicting blending weights of source views from the target scene. The advantages of this design include: (1) ensuring accurate geometry estimation for high-quality 3D reconstruction, and (2) enabling real-time rendering after baking the complex geometry branch into meshes and using the lightweight appearance branch as a Shader.  
+- We intriguingly find that a properly trained image-based rendering pipeline can directly lift 2D monocular priors to 3D novel views by reusing the blending weights predicted by the appearance branch and leverage this to enable generalizable, zero-shot, and multitask scene understanding.  
+- We demonstrate the adaptability of Omni-Recon by extending it to diverse downstream tasks, including: (1) a Nvdiffrast-based [31] real-time rendering pipeline using the extracted mesh, (2) parameter-efficient tuning (PET) of Omni-Recon on downstream generalizable scene understanding tasks, and (3) a new text-guided scene editing scheme by iteratively editing and reconstructing the source images from a 3D scene using 2D diffusion models.  
+- Extensive experiments demonstrate that our Omni-Recon can achieve state-of-the-art (SOTA) generalizable 3D surface reconstruction and scene understanding performance. Additionally, it can support real-time rendering of a new scene after rapid adaptation, along with easy-to-use 3D scene editing.
+
+Our Omni-Recon underscores the potentially wide usage of image-based rendering pipelines in diverse real-world 3D applications, which may have been overlooked by previous works. We believe that the insights we provide could ignite future innovations in more advanced generalizable rendering pipelines.
+
+# 2 Related Work
+
+Neural Radiance Fields. NeRFs [42], which represent the target scene as a continuous volume with density and view-dependent color, have garnered increasing attention thanks to their SOTA rendering quality when compared to prior approaches using explicit neural representations [24,39,60,66]. Subsequent works further improve NeRFs from various angles, such as enhancing their rendering quality [2,3,11,72], training NeRFs with sparse views [18,28,46,69,80,81], extending NeRFs to large-scale scenes [64,70,78,79,91], or applying NeRFs to other tasks, such as generative modeling [7,47,58,74], dynamic scenes [6,33,49, 50], or scene understanding [21,30,73,93].
+
+Generalizable NeRFs. Vanilla NeRFs rely on per-scene optimization, which limits their ability to perform instant reconstruction. To address this limitation, generalizable NeRF designs [10, 13, 29, 38, 54, 68, 75, 77, 82, 88] achieve cross-scene generalization by conditioning vanilla NeRFs on the source views from the target new scene. Specifically, [68, 75, 77, 88] take the extracted scene features from the source views as inputs to reconstruct the radiance field via a one-shot forward
+
+pass. Subsequent works further improve cross-scene generalization capabilities by enhancing scene geometry estimation [10, 29, 38] and by introducing attention mechanisms and mixture-of-experts into NeRF backbone design [15, 36, 75]. Despite the promise of generalizable NeRFs, they still fall short in terms of rendering efficiency due to their huge computational complexity, which entails costly inference for each sampled point along the ray. While some generalizable surface reconstruction methods [34, 55] can support mesh extraction, a costly volumetric rendering process, independent of the extracted mesh, is still necessary for rendering novel views. This poses a hindrance to their deployment in 3D applications that require real-time rendering. Furthermore, their potential for zero-shot scene understanding and scene editing remains unexplored.
+
+Efficient scene representations. Various scene representations have been proposed to accelerate NeRFs' training and inference process. To speed up NeRF training, [8,9,20,43,62] integrate explicit neural representations with volumetric rendering. In parallel, to speed up NeRF inference/rendering, [35,52,53] attempt to reduce the complexity of the multi-layer perceptron (MLP) model in NeRF. In addition, [22, 25, 27, 37, 53, 59, 87] explore the free space in a target 3D scene to improve the sampling efficiency and thus reduce unnecessary MLP inference. Motivated by the insight of deferred rendering [67], [12, 25, 65, 85] pre-compute scene features offline and thus require less computation at run-time. In particular, [12, 65, 85] represents NeRF as a set of textured polygons, where the textures can be pre-computed and thus the NeRF pipeline can be efficiently executed in modern graphics hardware. However, these efficient scene representations still necessitate costly per-scene optimization, lacking generalization capabilities.
+
+# 3 Omni-Recon: The General-Purpose NeRF Backbone
+
+To develop general-purpose NeRFs for diverse 3D tasks, in this section, we design a general-purpose NeRF backbone and demonstrate its SOTA generalizable 3D reconstruction capability. We will leverage this backbone to enable zero-shot multitask scene understanding in Sec. 4 and adapt it to diverse downstream 3D tasks, including real-time rendering, PET for 3D scene understanding, and text-guided scene editing in Sec. 5.
+
+# 3.1 Backbone Design Overview
+
+Design principles. Our design is driven by the following principles: (1) Similar to foundation models, the ability to instantly reconstruct a new scene with generalizable capabilities is essential. Therefore, we leverage image-based rendering [10,38,77], which lifts 2D image features into their 3D counterparts, as our backbone design and uncover their decent capabilities in diverse 3D applications, which may have been overlooked by previous works. (2) Considering a wide range of 3D applications that require real-time rendering, it is highly desirable that our backbone design can be seamlessly converted into efficient 3D representations, particularly meshes, to enable real-time rendering.
+
+Our backbone design. As shown in Fig. 1, the backbone model in OmniRecon leverages image-based rendering with two decoupled branches: one complex transformer-based geometry branch that progressively fuses geometry and
+
+![](images/5556299a659e0430e950b9d7ef37408a3da658d642f050271fb4e782aae15406.jpg)  
+Fig. 1: An overview of our Omni-Recon framework.
+
+appearance features to accurately predict an SDF, and one lightweight appearance branch for estimating blending weights of source views. Specifically, the progressive feature fusion in the geometry branch empowers accurate geometry estimation for high-quality 3D reconstruction, and the decoupling between geometry and appearance enables real-time rendering after baking the complex geometry branch into meshes, with the lightweight appearance branch serving as a shader. Additionally, the nature of image-based rendering, when equipped with accurate geometry and appearance estimation, can enable a wide range of downstream 3D applications, as shown in Sec. 4 and Sec. 5.
+
+# 3.2 The Overall Image-Based Rendering Pipeline
+
+Following generalizable NeRFs [10,38,77,88], our NeRF backbone is conditioned on source views from the target scene. This is achieved by estimating the geometry and appearance based on the extracted features of the source views from the target scene, which is elaborated as follows.
+
+Acquire geometry features. Given  $N$  source views  $\{I_i\}_{i=1}^N \in \mathbb{R}^{H \times W \times 3}$  from the target scene, we utilize a CNN encoder [34, 55] to extract source features  $\{\mathbf{F}_i\}_{i=1}^N \in \mathbb{R}^{H \times W \times C}$  from the source views. Next, we construct a 3D feature volume  $V \in \mathbb{R}^{M \times M \times M \times C}$ , which serves similar roles as cost volumes for multiview stereo [10, 83], to aggregate geometry information from different source views, following [44, 55, 63]. Specifically, we divide the bounding volume of the scene into  $M^3$  voxels and project each voxel center onto  $N$  source views, where the mean and variance of  $N$  source features are computed and concatenated as the corresponding voxel feature. We then leverage a 3D U-Net [14] to further enhance the feature volume to acquire the final geometry feature volume  $V$ .
+
+Acquire appearance features. For each sampled 3D point  $\mathbf{p}(t) = \mathbf{o} + t\mathbf{d}$  along the ray emitted from the target pixel on the novel view with camera origin  $\mathbf{o}$
+
+and direction  $\mathbf{d}$ , we project it on the source views to acquire the projected colors  $\{\mathbf{c}_i\}_{i=1}^N$  and appearance features  $\{\mathbf{f}_i\}_{i=1}^N$  using bilinear interpolation.
+
+Geometry and appearance estimation. For more accurate reconstruction of the scene geometry and mitigation of the intrinsic color-density ambiguity [76], we estimate the per-point SDF and model the density as a function of SDF, following [40,55,76]. For appearance, we estimate the per-point radiance through color blending, i.e., predicting the blending weights of the projected colors on the source views to compose the per-point color, following [38,77,88]. The geometry and appearance are modeled using separate branches and can be formulated as:
+
+$$
+s = \mathbf {M} _ {s d f} (\{\mathbf {f} _ {i} \} _ {i = 1} ^ {N}, V), \quad \{\omega_ {i} \} _ {i = 1} ^ {N} = \mathbf {M} _ {c o l o r} (\{\mathbf {f} _ {i} \} _ {i = 1} ^ {N}, \mathbf {d}), \qquad (1)
+$$
+
+where  $s$  is the SDF,  $\{\omega_i\}_{i=1}^N$  are the blending weights, and  $\mathbf{M}_{sdf}$  and  $\mathbf{M}_{color}$  are the geometry and appearance branches, respectively. The per-point radiance can be derived as the weighted sum  $\hat{\mathbf{c}} = \sum_{i=1}^{N} \omega_i \mathbf{c}_i$  across  $N$  source views.
+
+For SDF-based volumetric rendering, we adopt the formulation in NeuS [76]. Specifically, the color is accumulated along the  $K$  sampled points along each ray:
+
+$$
+\hat {\mathbf {C}} = \sum_ {j = 1} ^ {K} T _ {j} \alpha_ {j} \hat {\mathbf {c}} _ {j}, \quad \alpha_ {j} = 1 - \exp \left(- \int_ {t _ {j}} ^ {t _ {j + 1}} \rho (t) d t\right), \tag {2}
+$$
+
+where  $T_{j} = \prod_{n=1}^{j-1}(1 - \alpha_{n})$  is the accumulated transmittance and  $\rho(t)$  is the density determined by the estimated SDF, following the definition in NeuS [76]. Complexities of the two branches. When predicting the radiance of a sampled 3D point, the colors of valid projections on the source views are already close to the radiance of this 3D point. In light of this, we employ a lightweight  $\mathbf{M}_{color}$ , consisting of three MLP layers, for efficient blending weight prediction. In contrast, the geometry branch should be allocated with sufficient complexity for accurately estimating the occlusion effects among different sampled points along the same ray as well as that among the sampled points and the source views. We introduce the design of the geometry branch in the next subsection.
+
+# 3.3 The Proposed Transformer-based Geometry Branch
+
+Overview. Our geometry branch leverages transformer modules [71] to progressively fuse geometry and appearance features, thus properly handling the two occlusion effects mentioned above. Specifically, for the  $K$  sampled 3D points along the ray, we acquire their geometry features  $\{\mathbf{v}_k\}_{k=1}^K$  from the geometry feature volume  $V$ , which are sequentially processed through  $B$  blocks ( $B = 2$  in our design), each consisting of a geometry transformer, an appearance transformer, and an occlusion transformer, to produce the per-point SDF.
+
+The geometry transformer. The geometry transformer fuses the geometry features into its inputs by using  $\{\mathbf{v}_k\}_{k=1}^K$  as the key and value in a cross-attention scheme [71] and performs attention across the sampled points along the same ray to model their occlusion effects. This process can be formulated as follows:
+
+$$
+\mathbf {M} _ {s d f} ^ {g e o} (\mathbf {x}, \{\mathbf {v} _ {k} \} _ {k = 1} ^ {K}) = C r o s s A t t e n t i o n (\mathbf {q} = \mathbf {x}, \mathbf {k} = \mathbf {v} = \{\mathbf {v} _ {k} \} _ {k = 1} ^ {K}), \qquad (3)
+$$
+
+where  $\mathbf{x}$  is the input to the geometry transformer, which is exactly the geometry features themselves for the geometry transformer in the first block, and  $\mathbf{q}$ ,  $\mathbf{k}$ , and  $\mathbf{v}$  are the query, key, and value of the attention scheme [71], respectively.
+
+The appearance transformer. The appearance transformer integrates the appearance features  $\{\mathbf{f}_i\}_{i=1}^N$  into the outputs of the geometry transformer, considering the potential occlusions among the sampled points and the source views that may cause invalid projections. This integration is achieved using subtraction attention [75,92], which is more effective for geometric relationship reasoning [75,92], with  $\{\mathbf{f}_i\}_{i=1}^N$  as the key and value. Specifically, subtraction attention computes the attention scores between the input query features and each source view in the key features, which are then used to aggregate different source views in the value features into new output features with the same dimension as the input query features. This process can be formulated as follows:
+
+$$
+\mathbf {M} _ {s d f} ^ {a p p r} (\mathbf {x}, \{\mathbf {f} _ {i} \} _ {i = 1} ^ {N}) = S u b A t t e n t i o n (\mathbf {q} = \mathbf {x}, \mathbf {k} = \mathbf {v} = \{\mathbf {f} _ {i} \} _ {i = 1} ^ {N}), \tag {4}
+$$
+
+where  $\mathbf{x}$  is the output of  $\mathbf{M}_{sdf}^{geo}$ . The detailed formulation of subtraction attention is provided in the supplementary materials.
+
+The occlusion transformer. We further integrate an occlusion transformer that performs self-attention across sampled points along the same ray to model their occlusion effects explicitly:
+
+$$
+\mathbf {M} _ {s d f} ^ {\text {o c c}} (\mathbf {x}) = \text {S e l f A t t e n t i o n} (\mathbf {q} = \mathbf {k} = \mathbf {v} = \mathbf {x}), \tag {5}
+$$
+
+where  $\mathbf{x}$  is the output of  $\mathbf{M}_{sdf}^{app}$ . We apply another MLP on top of the output features of the last  $M_{sdf}^{occ}$  module to predict the per-point SDF  $s$  in Eq. 1.
+
+# 3.4 Training Objectives
+
+Our NeRF backbone is trained using two objectives: (1) a photometric loss  $\mathcal{L}_{rgb}$  between the rendered images and the ground-truth images, and (2) a depth loss  $\mathcal{L}_{depth}$  between the rendered depth  $\hat{\mathbf{D}} = \sum_{j=1}^{K} T_j \alpha_j t$  and the ground-truth depth. The overall objective can be formulated as follows:
+
+$$
+\mathcal {L} = \mathcal {L} _ {\text {c o l o r}} + \beta \mathcal {L} _ {\text {d e p t h}}, \tag {6}
+$$
+
+where  $\mathcal{L}_{rgb} = \frac{1}{R}\sum_{\mathbf{r} = 1}^{R}\left\| \hat{\mathbf{C}}_{\mathbf{r}} - \mathbf{C}_{\mathbf{r}}\right\|_{2}^{2}$  with  $R$  denoting the number of rendered pixels,  $\mathcal{L}_{depth} = \frac{1}{R}\sum_{\mathbf{r} = 1}^{R}\left\| \hat{\mathbf{D}}_{\mathbf{r}} - \mathbf{D}_{\mathbf{r}}\right\|_{2}^{2}$ , and  $\beta$  is set as 1.
+
+# 3.5 Evaluation: Generalizable 3D Reconstruction
+
+Setup. We assess the backbone of Omni-Recon by evaluating its generalizable 3D reconstruction capability. Datasets: Following [10, 34, 40, 55], we adopt the DTU dataset [1], which comprises 124 different scenes and 7 distinct lighting conditions. For testing, 15 scenes are utilized, while the remaining scenes are allocated for training, following the dataset split in [34, 40, 55]. Training settings: During training, we employ  $N = 4$  source views with a resolution of  $640 \times 512$ . The ray number per batch and batch size are set to 1024 and 2, respectively,
+
+Table 1: Benchmark the quantitative performance in sparse view mesh reconstruction in terms of Chamfer distance (the lower, the better) on 15 testing scenes on DTU. The best scores are in bold, and the second-best are underlined.  
+
+<table><tr><td>Method</td><td>Mean</td><td>24</td><td>37</td><td>40</td><td>55</td><td>63</td><td>65</td><td>69</td><td>83</td><td>97</td><td>105</td><td>106</td><td>110</td><td>114</td><td>118</td><td>122</td></tr><tr><td>COLMAP [57]</td><td>1.52</td><td>0.90</td><td>2.89</td><td>1.63</td><td>1.08</td><td>2.18</td><td>1.94</td><td>1.61</td><td>1.30</td><td>2.34</td><td>1.28</td><td>1.10</td><td>1.42</td><td>0.76</td><td>1.17</td><td>1.14</td></tr><tr><td>MVSNet [83]</td><td>1.22</td><td>1.05</td><td>2.52</td><td>1.71</td><td>1.04</td><td>1.45</td><td>1.52</td><td>0.88</td><td>1.29</td><td>1.38</td><td>1.05</td><td>0.91</td><td>0.66</td><td>0.61</td><td>1.08</td><td>1.16</td></tr><tr><td>IDR [86]</td><td>3.39</td><td>4.01</td><td>6.40</td><td>3.52</td><td>1.91</td><td>3.96</td><td>2.36</td><td>4.85</td><td>1.62</td><td>6.37</td><td>5.97</td><td>1.23</td><td>4.73</td><td>0.91</td><td>1.72</td><td>1.26</td></tr><tr><td>VolSDF [84]</td><td>3.41</td><td>4.03</td><td>4.21</td><td>6.12</td><td>0.91</td><td>8.24</td><td>1.73</td><td>2.74</td><td>1.82</td><td>5.14</td><td>3.09</td><td>2.08</td><td>4.81</td><td>0.60</td><td>3.51</td><td>2.18</td></tr><tr><td>UNISURF [48]</td><td>4.39</td><td>5.08</td><td>7.18</td><td>3.96</td><td>5.30</td><td>4.61</td><td>2.24</td><td>3.94</td><td>3.14</td><td>5.63</td><td>3.40</td><td>5.09</td><td>6.38</td><td>2.98</td><td>4.05</td><td>2.81</td></tr><tr><td>NeuS [76]</td><td>4.00</td><td>4.57</td><td>4.49</td><td>3.97</td><td>4.32</td><td>4.63</td><td>1.95</td><td>4.68</td><td>3.83</td><td>4.15</td><td>2.50</td><td>1.52</td><td>6.47</td><td>1.26</td><td>5.57</td><td>6.11</td></tr><tr><td>PixelNeRF [88]</td><td>6.18</td><td>5.13</td><td>8.07</td><td>5.85</td><td>4.40</td><td>7.11</td><td>4.64</td><td>5.68</td><td>6.76</td><td>9.05</td><td>6.11</td><td>3.95</td><td>5.92</td><td>6.26</td><td>6.89</td><td>6.93</td></tr><tr><td>IBRNet [77]</td><td>2.32</td><td>2.29</td><td>3.70</td><td>2.66</td><td>1.83</td><td>3.02</td><td>2.83</td><td>1.77</td><td>2.28</td><td>2.73</td><td>1.96</td><td>1.87</td><td>2.13</td><td>1.58</td><td>2.05</td><td>2.09</td></tr><tr><td>MVSNeRF [10]</td><td>2.09</td><td>1.96</td><td>3.27</td><td>2.54</td><td>1.93</td><td>2.57</td><td>2.71</td><td>1.82</td><td>1.72</td><td>2.29</td><td>1.75</td><td>1.72</td><td>1.47</td><td>1.29</td><td>2.09</td><td>2.26</td></tr><tr><td>SparseNeuS [40]</td><td>1.96</td><td>2.17</td><td>3.29</td><td>2.74</td><td>1.67</td><td>2.69</td><td>2.42</td><td>1.58</td><td>1.86</td><td>1.94</td><td>1.35</td><td>1.50</td><td>1.45</td><td>0.98</td><td>1.86</td><td>1.87</td></tr><tr><td>VolRecon [55]</td><td>1.38</td><td>1.20</td><td>2.59</td><td>1.56</td><td>1.08</td><td>1.43</td><td>1.92</td><td>1.11</td><td>1.48</td><td>1.42</td><td>1.05</td><td>1.19</td><td>1.38</td><td>0.74</td><td>1.23</td><td>1.27</td></tr><tr><td>ReTR [34]</td><td>1.17</td><td>1.05</td><td>2.31</td><td>1.44</td><td>0.98</td><td>1.18</td><td>1.52</td><td>0.88</td><td>1.35</td><td>1.30</td><td>0.87</td><td>1.07</td><td>0.77</td><td>0.59</td><td>1.05</td><td>1.12</td></tr><tr><td>Omni-Recon (Ours)</td><td>1.13</td><td>0.91</td><td>2.13</td><td>1.52</td><td>0.93</td><td>1.09</td><td>1.70</td><td>0.84</td><td>1.29</td><td>1.20</td><td>0.83</td><td>1.04</td><td>0.81</td><td>0.55</td><td>1.05</td><td>1.05</td></tr></table>
+
+following [34,55]. Mesh extraction: We utilize TSDF fusion [45,63] to reconstruct scene meshes from predicted source view depths, following [34,55]. More detailed model/training settings can be found in the supplementary materials.
+
+Baselines. We primarily benchmark against SOTA generalizable neural implicit reconstruction methods [16, 34, 55], along with generalizable neural rendering methods [10, 77, 88], per-scene optimization methods [48, 76, 84, 86], and multiview stereo methods [57, 83]. We directly report their official results in [34, 55].
+
+Sparse view reconstruction. We benchmark the performance of sparse view reconstruction using three views from each testing scene in DTU, following the setting in [16, 34, 55]. As shown in Tab. 1, we can observe that (1) Our Omni-Recon can achieve new SOTA reconstruction performance, with the lowest Chamfer distance averaged over all scenes. Additionally, as visualized in Fig. 2 (Row 1), our reconstructed mesh can exhibit smooth surfaces and correctly maintain the structure, e.g., the hole in the mouth. (2) Our method achieves the best reconstruction performance in 10 out of 15 scenes, indicating its general effectiveness across diverse scenes.
+
+Generalization to more diverse scenes. To demonstrate the generalization capability on more diverse scenes with larger domain shifts compared to the training scenes, we further benchmark the mesh reconstruction quality on the NeRF-Synthetic dataset [41] and NSVF-Synthetic [37] under a full-view reconstruction setting, i.e., the depth of each source view from each scene's training set is estimated and fused into a mesh using TSDF fusion [45, 63]. As visualized in Fig. 2 (Rows 2-4), we observe that our Omni-Recon can deliver notably higher-quality meshes with better-maintained structures, fewer holes, and smoother surfaces compared to the strongest baselines ReTR [34] and VolRecon [55]. The decent generalization capability of our method is attributed to the proper decoupling and fusion of geometry and appearance features in our backbone. More visualizations and ablation studies, such as the contributions of each backbone component, are provided in the supplementary materials.
+
+Rendering quality. We further benchmark the performance of generalizable novel view rendering on the test scenes from DTU. As shown in Tab. 2, our
+
+![](images/253e7b23dc30b8c24932e32989d56897591d241547df22e0ec08fea8de4aceb3.jpg)  
+Fig. 2: Visualize the reconstructed mesh of our Omni-Recon and the two strongest baselines [34, 55]. Row 1: Scan65 from the test scenes of DTU; Rows 2-4: Scenes from NeRF-Synthetic [41] and NSVF-Synthetic [37], which present relatively challenging cases due to domain shifts w.r.t. the training scenes from DTU.
+
+Table 2: Benchmark the quality of novel view rendering in terms of PSNR on DTU.  
+
+<table><tr><td>Method</td><td>Mean.</td><td>24</td><td>37</td><td>40</td><td>55</td><td>63</td><td>65</td><td>69</td><td>83</td><td>97</td><td>105</td><td>106</td><td>110</td><td>114</td><td>118</td><td>122</td></tr><tr><td>VolRecon [55]</td><td>24.58</td><td>22.33</td><td>20.59</td><td>21.53</td><td>23.72</td><td>24.2</td><td>23.65</td><td>24.47</td><td>22.77</td><td>23.54</td><td>22.62</td><td>26.89</td><td>27.44</td><td>25.76</td><td>30.14</td><td>29.19</td></tr><tr><td>ReTR [34]</td><td>25.59</td><td>24.32</td><td>21.84</td><td>23.4</td><td>24.56</td><td>26.31</td><td>24.5</td><td>24.63</td><td>24.3</td><td>24.58</td><td>23.85</td><td>27.84</td><td>27.97</td><td>26.76</td><td>30.03</td><td>28.96</td></tr><tr><td>Omni-Recon</td><td>26.32</td><td>24.77</td><td>22.33</td><td>23.92</td><td>25.56</td><td>26.37</td><td>24.75</td><td>25.19</td><td>24.94</td><td>24.92</td><td>25.06</td><td>28.39</td><td>28.63</td><td>28.01</td><td>31.49</td><td>30.5</td></tr></table>
+
+method achieves the highest PSNR, e.g., a  $+0.73$  and  $+1.74$  PSNR improvement over ReTR and VolRecon, respectively. As will be demonstrated in Sec. 5.4, this PSNR can be notably boosted with super-fast Nvdiffrast-based finetuning [31].
+
+# 4 Omni-Recon: Enable Zero-shot Scene Understanding
+
+To enable the understanding of various 3D scene properties, one approach is to jointly train the NeRF backbone on all target tasks. However, this approach is not scalable due to the diversity of scene properties, such as semantic segmentation, edge detection, and keypoint prediction, making it costly to cover new properties of interest. Therefore, it is highly desirable to enable zero-shot scene understanding for new tasks that were not seen during training.
+
+# 4.1 Our Strategy: The Predict-then-Blend Strategy
+
+Our hypothesis. We hypothesize that with accurate geometry and appearance estimation, the blending weights initially learned for radiance can be repurposed for other scene understanding tasks. In essence, just as blending source colors
+
+![](images/2499968a06e76450b64793bc71a72b1a6d2e791f72f49d34b6b6f2244b7a3f6b.jpg)  
+Fig. 3: Benchmark our predict-then-blend strategy with the render-then-predict baseline across different scene understanding tasks on Replica [61] and ScanNet [17].
+
+yields radiance for each sampled 3D point, blending the properties of source views using the same weights can yield other properties. This hypothesis stems from the observation that scene appearance is closely related to other scene properties and thus regions with similar appearance are likely to share similar scene properties, such as semantics and edges. As such, the blending weights learned for appearance can also indicate the weighting for other scene properties.
+
+Our method. Motivated by this hypothesis, our Omni-Recon employs a predict-then-blend strategy to enable zero-shot multitask scene understanding by lifting 2D monocular priors to 3D novel views. Specifically, considering the prevalence of 2D monocular vision models, our method first utilizes pretrained 2D models to generate predictions (e.g., logits in semantic segmentation) on each source view, denoted as  $\{\mathbf{P}_i\}_{i=1}^N$ . Then, the property of each sampled point can be obtained by blending their projected predictions on source views  $\{\mathbf{p}_i\}_{i=1}^N$ , reusing the RGB blending weights, i.e.,  $\hat{\mathbf{p}} = \sum_{i=1}^{N} \omega_i \mathbf{p}_i$ . The final pixel-wise prediction can be derived using volumetric rendering similar to RGB reconstruction in Eq. 2.
+
+# 4.2 Evaluation: Zero-shot Multitask Scene Understanding
+
+Setup. To evaluate the zero-shot scene understanding capabilities of our model, we adopt two indoor 3D scene datasets: Replica [61] and ScanNet [17], following
+
+the dataset splits in [93] and [36], respectively. Specifically, we focus on four scene understanding tasks: semantic segmentation, edge detection, and two keypoint detection tasks defined in [89]. For these tasks, the 2D monocular priors are provided by [51], [5], and [56], respectively. We employ mIoU for the semantic segmentation task and  $\ell_1$  error for the other three tasks as default metrics.
+
+Baseline. An intuitive way to achieve zero-shot 3D scene understanding is to leverage existing 2D monocular models to predict the scene properties on top of the rendered images, a process referred to as a render-then-predict pipeline. We benchmark this strategy against our proposed predict-then-blend strategy.
+
+Zero-shot scene understanding benchmark. As shown in Tab. 3 and Fig. 3, we observe that (1) Our predict-then-blend strategy achieves decent scene understanding performance on unseen scenes and unseen tasks, which echoes our hypothesis in Sec. 4.1 that 2D monocular priors can be elevated to 3D novel views through
+
+Table 3: Benchmark the two zero-shot scene understanding strategies across tasks.  
+
+<table><tr><td>Dataset</td><td>Method</td><td>↑ Semantic</td><td>↓ Edge</td><td>↓ Key Point</td><td>↓ Key Point 3D</td></tr><tr><td rowspan="2">Replica [61]</td><td>Render-then -Predict</td><td>15.64</td><td>0.0456</td><td>0.1101</td><td>0.0470</td></tr><tr><td>Predict-then -Blend (Ours)</td><td>32.11</td><td>0.0412</td><td>0.0774</td><td>0.0176</td></tr><tr><td rowspan="2">ScanNet [17]</td><td>Render-then -Predict</td><td>41.32</td><td>0.0471</td><td>0.0568</td><td>0.0412</td></tr><tr><td>Predict-then -Blend (Ours)</td><td>61.11</td><td>0.0434</td><td>0.0424</td><td>0.0197</td></tr></table>
+
+blending weight reuse, thanks to the high correlation between scene appearance and other properties; (2) Compared to the render-then-predict scheme, our method achieves notably higher quantitative accuracy, e.g., a  $19.79\%$  higher mIoU on ScanNet, and consistently better visual effects across all tasks. We attribute this improvement to two aspects: (1) Our predict-then-blend strategy can avoid feeding noisy inputs caused by rendering errors to the 2D monocular models, thus reducing reliance on the robustness of monocular models and ensuring more accurate monocular priors; (2) The multiview information gathered by our strategy can contribute to a more accurate understanding of objects with limited observations under a monocular setting. More experiments on other vision models like CLIP-LSeg [32] are in the supplementary materials.
+
+# 5 Omni-Recon: Adaptability to Downstream 3D Tasks
+
+In addition to the generalization capabilities, we further demonstrate the adaptability of our Omni-Recon by extending it to three different types of downstream 3D applications. These demonstrations highlight the potentially broad utility of image-based rendering pipelines, which may have been overlooked by previous works, and can be generalized to a wider range of real-world 3D applications.
+
+# 5.1 Application 1: A New Real-time Rendering Pipeline
+
+To rapidly enable real-time rendering using Omni-Recon's pretrained backbone, we build a real-time rendering pipeline based on Nvdiffrast [31]. This involves baking the complex geometry branch into meshes using TSDF fusion [45,63] as discussed in Sec. 3.5, and using the lightweight appearance branch as a Shader.
+
+Our real-time rendering pipeline. As illustrated in Fig. 1 (bottom left), for any novel view of a new scene, we first transform its camera pose to clip space as required by Nvdiffrast [31]. Then, we perform a rasterization process to obtain the intersection point of each camera ray with the extracted mesh. Next, these
+
+intersection points are input into the shader to predict the color by blending their projected colors on the source views as described in Sec. 3.2. Thanks to the blending scheme, where valid projections can provide strong appearance clues, the rendering quality is often satisfactory even without finetuning.
+
+Joint mesh and Shader finetuning. To further enhance the rendering quality, we can jointly finetune the mesh and the Shader in a gradient-based manner using differentiable rendering enabled by Nvdiffrast [31]. Specifically, we finetune the location of each vertex in the mesh and the parameters of the lightweight Shader. Additionally, we periodically prune redundant mesh faces that cannot intersect with any camera rays in one training epoch. More details are provided in the supplementary materials.
+
+# 5.2 Application 2: Parameter-Efficient Tuning for 3D Understanding
+
+We explore the possibility of finetuning Omni-Recon for various downstream 3D tasks, utilizing a series of 3D scene understanding tasks as demonstrations. Specifically, we conduct PET [19] on top of Omni-Recon's pretrained backbone by incorporating low-rank adapters [26] into the linear layers within the transformer modules introduced in Sec. 3.3, and only tuning these adapters while keeping the pretrained backbone frozen. It is worth noting that when evaluating the performance of the tuned model, we assess it on new scenes that have never been encountered during both training and tuning, representing a generalizable scene understanding scenario [36].
+
+# 5.3 Application 3: Text-guided 3D Scene Editing
+
+We target text-guided 3D scene editing, which involves editing a 3D scene based on textual instructions. To achieve this, our Omni-Recon capitalizes on the nature of image-based rendering, allowing edits made to 2D source views to be propagated to 3D novel views. Specifically, leveraging the availability of powerful 2D diffusion models, we use publicly available ones to edit the 2D source views. However, doing so naively may result in inconsistent source views, as the same textual instruction may lead to different visualizations, thus degrading reconstruction quality. Inspired by [23], which iteratively edits the training images and retransmits a new NeRF to achieve scene editing, we address the inconsistent editing issue by proposing an iterative editing and reconstruction pipeline.
+
+The iterative editing and reconstruction pipeline. To ensure consistent 3D editing, we iterate between source view editing and reconstruction as follows: First, we edit each source view using the diffusion model InstructPix2Pix [4], which is guided by both textual instructions and the original image to maintain image fidelity. While the edited source views can faithfully follow the textual instructions, they may exhibit inconsistencies in the edited region. Next, we reconstruct each source view by conditioning on their nearby source views, as described in Sec. 3.2. This process improves the 3D consistency among source views at the expense of reduced image fidelity and less adherence to instructions. We then iterate through the editing and reconstruction steps to progressively enhance image fidelity, 3D consistency, and adherence to instructions. The resulting source views are finally used to reconstruct the entire scene.
+
+Table 4: Benchmark the rendering speed and rendering quality of our real-time rendering pipeline and the baselines [34,55] on DTU. The best PSNR is in bold, and the shortest finetuning time that our pipelines surpass the strongest baseline is underlined.  
+
+<table><tr><td>Method</td><td>FPS</td><td>Mean</td><td>24</td><td>37</td><td>40</td><td>55</td><td>63</td><td>65</td><td>69</td><td>83</td><td>97</td><td>105</td><td>106</td><td>110</td><td>114</td><td>118</td><td>122</td></tr><tr><td>VolRecon [55]</td><td>0.029</td><td>24.58</td><td>22.33</td><td>20.59</td><td>21.53</td><td>23.72</td><td>24.2</td><td>23.65</td><td>24.47</td><td>22.77</td><td>23.54</td><td>22.62</td><td>26.89</td><td>27.44</td><td>25.76</td><td>30.14</td><td>29.19</td></tr><tr><td>ReTR [34]</td><td>0.024</td><td>25.59</td><td>24.32</td><td>21.84</td><td>23.4</td><td>24.56</td><td>26.31</td><td>24.5</td><td>24.63</td><td>24.3</td><td>24.58</td><td>23.85</td><td>27.84</td><td>27.97</td><td>26.76</td><td>30.03</td><td>28.96</td></tr><tr><td>Ours w/o ft.</td><td rowspan="7">71.3(40.82)</td><td>22.96</td><td>20.12</td><td>19.71</td><td>22.27</td><td>22.78</td><td>24.55</td><td>21.77</td><td>21.51</td><td>26.72</td><td>22.33</td><td>24.49</td><td>22.52</td><td>22.93</td><td>24.68</td><td>23.84</td><td>24.12</td></tr><tr><td>Ours (ft. 10s)</td><td>25.68</td><td>21.42</td><td>21.68</td><td>24.06</td><td>24.12</td><td>28.19</td><td>24.10</td><td>23.95</td><td>31.65</td><td>24.41</td><td>28.15</td><td>25.63</td><td>25.85</td><td>26.15</td><td>26.82</td><td>26.89</td></tr><tr><td>Ours (ft. 20s)</td><td>27.21</td><td>22.63</td><td>22.92</td><td>25.12</td><td>25.42</td><td>30.03</td><td>25.95</td><td>26.16</td><td>33.19</td><td>26.22</td><td>30.36</td><td>27.44</td><td>27.04</td><td>26.93</td><td>29.15</td><td>29.65</td></tr><tr><td>Ours (ft. 30s)</td><td>27.78</td><td>23.2</td><td>23.26</td><td>25.54</td><td>25.7</td><td>30.59</td><td>26.83</td><td>26.96</td><td>33.66</td><td>26.47</td><td>30.73</td><td>28.14</td><td>27.70</td><td>27.1</td><td>30.17</td><td>30.65</td></tr><tr><td>Ours (ft. 1min)</td><td>28.34</td><td>24.69</td><td>24.11</td><td>25.76</td><td>26.05</td><td>30.93</td><td>27.66</td><td>27.49</td><td>33.68</td><td>27.07</td><td>30.97</td><td>28.51</td><td>28.54</td><td>27.35</td><td>31.17</td><td>31.54</td></tr><tr><td>Ours (ft. 3min)</td><td>28.95</td><td>25.2</td><td>24.32</td><td>25.94</td><td>26.16</td><td>32.15</td><td>28.99</td><td>27.88</td><td>34.94</td><td>27.35</td><td>31.62</td><td>28.93</td><td>28.97</td><td>27.56</td><td>31.70</td><td>32.49</td></tr><tr><td>Ours (ft. 5min)</td><td>29.02</td><td>25.34</td><td>24.36</td><td>25.63</td><td>26.21</td><td>32.16</td><td>29.33</td><td>27.81</td><td>34.94</td><td>27.32</td><td>31.74</td><td>29.04</td><td>29.05</td><td>27.69</td><td>31.74</td><td>32.89</td></tr></table>
+
+# 5.4 Evaluation: Effectiveness on Different Downstream 3D Tasks
+
+Real-time rendering. We evaluate our real-time rendering pipeline in Sec. 5.1 in terms of both rendering quality and rendering speed, measured on an NVIDIA A5000 GPU, using the DTU dataset. Specifically, we finetune the scene meshes, extracted by TSDF fusion as mentioned in Sec. 3.5, and the Shader in no more than 5 minutes and record the rendering quality at different time steps.
+
+Observations and analysis: As shown in Tab. 4, we can observe that (1) Without any finetuning, the rendering pipeline already exhibits certain rendering capabilities, despite the model backbone being trained using volumetric rendering with more than one sampled point; (2) Our rendering pipeline can be swiftly finetuned to significantly boost the PSNR. For instance, with just a 10-second finetuning, our rendering pipeline can match the average rendering quality of the strongest baseline, ReTR [34]; With 1-minute/2-minute finetuning, it can surpass ReTR by a +2.75 and +3.36 PSNR improvement on average, respectively; (3) Our rendering pipeline enables real-time rendering with 71.3 FPS, i.e., a >2458× speed-up over the baselines. Here, the assumption is that the feature extraction of each source view from a target scene can be done in a one-time effort and does not account for throughput. If we still consider this feature extraction latency for each rendering, the resulting 40.82 FPS still meets the real-time requirement.
+
+These experiments indicate that given a new scene, our Omni-Recon is promising to enable both instant and accurate reconstruction and real-time rendering.
+
+PET for 3D Understanding. To evaluate the adaptability of our Omni-Recon, we apply PET to it for various scene understanding tasks on ScanNet [17]. Specifically, we finetune a LoRA adaptor [26] for each task on a set of training scenes and evaluate on
+
+Table 5: Evaluate the effectiveness of PET on Omni-Recon and benchmark with SRay [36].  
+
+<table><tr><td>Method</td><td>↑ Sem. mIoU</td><td>↑ Sem. Total Acc</td><td>↑ Sem. Avg Acc</td><td>↓ Edge</td><td>↓ Key Point</td><td>↓ Key Point 3D</td></tr><tr><td>SRay [36]</td><td>57.15</td><td>78.24</td><td>62.55</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Ours (Zero-shot)</td><td>61.11</td><td>80.80</td><td>69.17</td><td>0.0434</td><td>0.0424</td><td>0.0197</td></tr><tr><td>Ours (PET + Zero-shot)</td><td>62.35</td><td>81.84</td><td>69.89</td><td>0.0342</td><td>0.0207</td><td>0.0132</td></tr></table>
+
+non-overlapping test scenes, representing a generalizable 3D understanding setting, following the dataset split described in [36]. We benchmark against the SOTA generalizable semantic segmentation work, SRay [36].
+
+Observations and analysis: As shown in Tab. 5, we can observe that (1) After PET, our model outperforms SRay in terms of generalizable semantic segmentation with a  $+5.20\%$  mIoU and a  $+7.34\%$  accuracy improvement; (2) Despite
+
+![](images/6c7be7d29fa88ab9cad67e961ec0abd8829608f9dfec4b17ab9ee1cd19f8a98c.jpg)  
+Fig. 4: Visualize the scene understanding performance on hard cases before/after PET.
+
+![](images/33fe245c26fdbe0ab18443afadf59a49d34d5e8ccb4aa221368daa5744f241c1.jpg)  
+Fig. 5: Three text-guided scene editing examples using our pipeline in Sec. 5.3.
+
+having limited trainable parameters, PET can enhance the scene understanding performance across tasks, especially on challenging cases visualized in Fig. 4, indicating the adaptability of Omni-Recon to diverse downstream tasks.
+
+Text-guided scene editing. We visualize the edited examples in Fig. 5, showcasing the ability to ensure both instruction-following and 3D consistency.
+
+# 6 Conclusion
+
+Motivated by the significant demand for supporting various functionalities required by different 3D applications through a unified NeRF model, our work aims to develop a general-purpose NeRF capable of handling a broad spectrum of 3D tasks. We propose a framework called Omni-Recon, which features a general-purpose NeRF model using image-based rendering with two separate branches, and demonstrate its integration with various 3D tasks. Specifically, our Omni-Recon can achieve SOTA generalizable 3D reconstruction quality, enable zero-shot multitask scene understanding, achieve SOTA scene understanding performance and real-time rendering after rapid adaptation, and support scene editing. Omni-Recon underscores the potential of image-based rendering pipelines in diverse real-world 3D applications, which may have been underestimated by prior studies, thus sparking advancements in future rendering pipelines.
+
+# Acknowledgement
+
+The work is supported by the National Science Foundation (NSF) through the SCH program (Award number: 1838873) and CoCoSys, one of the seven centers in JUMP 2.0, a Semiconductor Research Corporation (SRC) program sponsored by DARPA.
+
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+        "text": "Omni6DPose: A Benchmark and Model for Universal 6D Object Pose Estimation and Tracking",
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+        "text": "Jiyao Zhang $^{1,2,3*}$ , Weiyao Huang $^{1*}$ , Bo Peng $^{1*}$ , Mingdong Wu $^{1,2,3}$ , Fei Hu $^{1,2,3}$ , Zijian Chen $^{4}$ , Bo Zhao $^{5,6}$ , and Hao Dong $^{1,2,3\\dagger}$",
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+            "$^{1}$  Center on Frontiers of Computing Studies, School of Computer Science, Peking University  $^{2}$  PKU-Agibot Lab, School of Computer Science, Peking University",
+            "$^{3}$  National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University  $^{4}$  Waseda University  $^{5}$  Shanghai Jiao Tong University  $^{6}$  BAAI {jiyaozhang, sshwy, bo.peng, hofee}@stu.pku.edu.cn zijianchenwaseda@akane.waseda.jp bo.zhao@sjtu.edu.cn {wmingd, hao.dong}@pku.edu.cn"
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+        "text": "Abstract. 6D object pose estimation is crucial in the field of computer vision. However, it suffers from a significant lack of large-scale and diverse datasets, impeding comprehensive model evaluation and curtailing downstream applications. To address these issues, this paper introduces Omni6DPose, a substantial benchmark featured by its diversity in object categories, large scale, and variety in object materials. Omni6DPose is divided into three main components: ROPE (Real 6D Object Pose Estimation Dataset), which includes 332K images annotated with over 1.5M annotations across 581 instances in 149 categories; SOPE(Simulated 6D Object Pose Estimation Dataset), a simulated training set created by mixed reality and physics-based depth simulation; and PAM(Pose Aligned 3D Models), the manually aligned real scanned objects used in ROPE and SOPE. Omni6DPose is inherently challenging due to the substantial variations and ambiguities. To address this issue, we introduce GenPose++, an enhanced version of the SOTA category-level 6D object pose estimation framework, incorporating two pivotal improvements: Semantic-aware feature extraction and Clustering-based aggregation. Moreover, we provide a comprehensive benchmarking analysis to evaluate the performance of previous methods on this new large-scale dataset in the realms of 6D object pose estimation and pose tracking.",
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+        "text": "Keywords: Benchmark  $\\cdot$  object pose estimation  $\\cdot$  object pose tracking",
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+        "type": "text",
+        "text": "1 Introduction",
+        "text_level": 1,
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+        "text": "6D object pose estimation [16,28,45] and pose tracking [15,21] from single images is an essential task in computer vision, holding immense potential for applications in robotics [1] and augmented reality/virtual reality (AR/VR) [19]. Over recent decades, the domain has experienced significant advancements, primarily dominated by data-driven learning approaches. Analogous to the pivotal role of data in learning-based 2D foundation tasks, high-quality, comprehensive datasets are paramount in the context of 6D object pose estimation and tracking.",
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+        "type": "page_footnote",
+        "text": "*: equal contributions, †: corresponding author",
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+        "image_caption": [
+            "Fig. 1: We introduce Omni6DPose for universal 6D object pose estimation and tracking, which comprises three key components: (1) ROPE (right), a large-scale real-world dataset for evaluation. (2) SOPE (left), a vast synthetic dataset generated through mixed reality and physics-based simulation for training. (3) PAM (center), a collection of pose-aligned 3D object models. Omni6DPose is featured by its diversity in object categories, large scale, and variety in object materials."
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+        "text": "Today 6D object pose estimation is studied under two lenses: instance-level and category-level. In instance-level settings, datasets such as Linemod [10], YCB-Video [36], and T-LESS [11] have gained widespread acceptance as benchmarks. These datasets are distinguished by their focus on detailed, individual object instances, thereby enabling algorithms to precisely learn and predict the poses of specific items. On the other hand, category-level pose estimation emphasizes generalization across different items within a particular object category. The NOCS [28] dataset stands out as the most widely used in the category-level object pose estimation field, providing a simulated dataset for training and a small-scale real-world dataset for evaluation. Despite their contributions to advancing the field, these datasets present limitations due to their small scale in terms of instances or categories. This results in two significant challenges:",
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+            "1. It hampers comprehensive evaluation of different models' performance, limiting the development of research in this field.",
+            "2. It restricts the applicability of research findings across diverse domains, due to the limited variety of object instances or categories represented."
+        ],
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+        "type": "text",
+        "text": "To address the aforementioned challenges and drive advancements in this field, this paper introduces Omni6DPose, a universal 6D object pose estimation dataset featured by its diversity in object categories, large scale, and variety in object materials. Omni6DPose is segmented into three principal components: (1) ROPE (Real 6D Object Pose Estimation Dataset), which encompasses  $332\\mathrm{K}$  images annotated with over 1.5M annotations across 581 instances in 149 categories; (2) SOPE (Simulated 6D Object Pose Estimation Dataset), comprising  $475\\mathrm{K}$  images generated in a mixed reality setting with depth simulation, furnished with over 5M annotations spanning 4162 instances in the same 149",
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+        "text": "2",
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+    {
+        "type": "header",
+        "text": "J. Zhang, W. Huang, B. Peng, M. Wu, F. Hu, Z. Chen, B. Zhao, H. Dong",
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+            "Fig. 2: ROPE dataset visualization. In the figure, bounding boxes are colored according to the coordinates in the object's coordinate system."
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+    {
+        "type": "text",
+        "text": "categories. The mixed reality bridges the semantic sim2real gap, while the depth sensor simulation closes the geometric sim2real gap; (3) PAM(Pose Aligned 3D Models), the manually aligned, real scanned objects utilized in both ROPE and SOPE, enabling the generation of diverse downstream task data.",
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+    {
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+        "text": "Omni6DPose poses inherent challenges due to its considerable variations, diverse materials, and inherent ambiguities, which reflect the complexities encountered in real-world applications. Figure 2 illustrates some examples from ROPE. To tackle these issues, we introduce GenPose++, which incorporates GenPose [43] with two pivotal improvements: Semantic-aware feature extraction and Clustering-based aggregation, tailored specifically to the nuances of the Omni6DPose in question. Furthermore, as a Universal 6D object pose estimation dataset, this paper also offers a comprehensive benchmarking analysis to assess the performance of existing methods on category-level 6D object pose estimation and pose tracking. We summarize our contributions as follows:",
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+        "list_items": [
+            "1. We present Omni6DPose, a comprehensive 6D object pose estimation dataset with extensive categories, instance diversity, and material variety.",
+            "2. We propose a real data collection pipeline and a simulation framework for generating synthetic data with low semantic and geometry sim2real gaps.",
+            "3. We introduce GenPose++ for category-level 6D object pose estimation and tracking, demonstrating SOTA performance on Omni6DPose."
+        ],
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+    {
+        "type": "text",
+        "text": "2 Related work",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "For 6D object pose estimation, there are two main branches: instance-level and category-level. Instance-level estimation is tested on seen objects or on unseen objects with reference, while category-level estimation is tested on unseen instances of known categories without CAD models. In this section, we review and compare existing datasets to our large-scale category-level dataset and review relevant algorithms for category-level pose estimation and tracking.",
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+    {
+        "type": "text",
+        "text": "2.1 6D Object Pose Estimation Datasets",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "Following the outlined branches of 6D object pose estimation, we have reviewed datasets corresponding to both instance-level and category-level 6D object pose estimation. A comparative analysis of these datasets is provided in Table 1.",
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+    {
+        "type": "header",
+        "text": "Omni6DPose",
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+    {
+        "type": "page_number",
+        "text": "3",
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+        "table_caption": [
+            "Table 1: This table compares datasets for 6D object pose estimation, focusing on object category count, reality of the data, data modalities (RGB, Depth, IR), and object attributes such as quantity, CAD model availability, and inclusion of transparent and specular objects. It also details video characteristics by number and marker presence, along with image and annotation counts. 'Wild6D' refers specifically to the test split of the Wild6D dataset, as the training data does not provide annotations. The symbol '-' indicates the absence of a particular feature within the dataset."
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td rowspan=\"2\">Dataset</td><td rowspan=\"2\" colspan=\"2\">Cat. Real</td><td colspan=\"3\">Modality</td><td colspan=\"4\">Object</td><td rowspan=\"2\">Marker-free Vid.</td><td rowspan=\"2\">Img.</td><td rowspan=\"2\">Anno.</td></tr><tr><td>RGB</td><td>Depth IR</td><td>IR</td><td colspan=\"4\">Num. CAD Trans. Spec.</td></tr><tr><td>CAMERA [28]</td><td>6</td><td>X</td><td>✓</td><td>✓</td><td>X</td><td>1085</td><td>✓</td><td>X</td><td>X</td><td>✓</td><td>-</td><td>300K</td></tr><tr><td>SOPE(Ours)</td><td>149</td><td>X</td><td>✓</td><td>✓</td><td>✓</td><td>4162</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>-</td><td>475K</td></tr><tr><td>YCB-Video [36]</td><td>-</td><td>✓</td><td>✓</td><td>✓</td><td>X</td><td>21</td><td>✓</td><td>X</td><td>X</td><td>✓</td><td>92</td><td>133K</td></tr><tr><td>T-LESS [11]</td><td>-</td><td>✓</td><td>✓</td><td>✓</td><td>X</td><td>30</td><td>✓</td><td>X</td><td>X</td><td>X</td><td>20</td><td>48K</td></tr><tr><td>Linemod [10]</td><td>-</td><td>✓</td><td>✓</td><td>✓</td><td>X</td><td>15</td><td>✓</td><td>X</td><td>X</td><td>X</td><td>-</td><td>18K</td></tr><tr><td>StereoOBJ-1M [17]</td><td>-</td><td>✓</td><td>✓</td><td>X</td><td>X</td><td>18</td><td>✓</td><td>✓</td><td>✓</td><td>X</td><td>182</td><td>393K</td></tr><tr><td>REAL275 [28]</td><td>6</td><td>✓</td><td>✓</td><td>✓</td><td>X</td><td>42</td><td>✓</td><td>X</td><td>X</td><td>X</td><td>18</td><td>8K</td></tr><tr><td>PhoCal [30]</td><td>8</td><td>✓</td><td>✓</td><td>✓</td><td>X</td><td>60</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>24</td><td>3.9K</td></tr><tr><td>HouseCat6D [12]</td><td>10</td><td>✓</td><td>✓</td><td>✓</td><td>X</td><td>194</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>41</td><td>23.5K</td></tr><tr><td>Wild6D* [9]</td><td>5</td><td>✓</td><td>✓</td><td>✓</td><td>X</td><td>162</td><td>X</td><td>(��)</td><td>X</td><td>✓</td><td>486</td><td>10K</td></tr><tr><td>PACE [41]</td><td>44</td><td>✓</td><td>✓</td><td>✓</td><td>X</td><td>576</td><td>✓</td><td>X</td><td>X</td><td>✓</td><td>300</td><td>55K</td></tr><tr><td>ROPE(Ours)</td><td>149</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>581</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>363</td><td>332K</td></tr></table>",
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+    {
+        "type": "text",
+        "text": "Instance-level 6D object pose estimation dataset. LineMod [10] is one of the most used datasets, providing non-temporal RGB-D images and ground truth pose annotations. YCB-Video [36] provides RGB-D videos and annotations, enabling pose-tracking approaches. T-LESS [11] features texture-less objects with symmetries and mutual similarities. StereoOBJ-1M [17] achieves a leap in dataset scale and features transparent and reflective objects. While these datasets are extensive in terms of image and annotation count, they are limited in the diversity of instances they cover. For example, StereoOBJ-1M comprises 339K frames and 1.5M annotations, yet it includes only 18 unique object instances.",
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+    {
+        "type": "text",
+        "text": "Category-level 6D object pose estimation dataset. NOCS [28] provides the first benchmark in category-level pose estimation, while Wild6D [9] addresses the scalability issue of datasets by leveraging unlabeled and synthetic data. PhoCal [30] focuses on photometrically challenging objects and HouseCat6D [12] offers diverse scenes, viewpoints, and grasping annotations. However, these datasets cover only a limited number of categories, even the most extensive dataset, HouseCat6D, includes merely ten categories. PACE [41], a concurrent work, focusing on object pose estimation in complex scenes. In contrast, our datasets, SOPE and ROPE, set new benchmarks by offering the widest range of categories and featuring objects with diverse materials, thereby enhancing dataset diversity and realism for pose estimation research.",
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+    {
+        "type": "text",
+        "text": "2.2 6D Object Pose Estimation and Tracking Algorithm",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "Category-level 6D object pose estimation. Category-level 6D object pose estimation [28,39,40] aims to estimate unseen instance poses within the same category. NOCS [28] introduces a normalized object coordinate space for pose prediction without CAD models, while SPD [29] and SGPA [3] utilize category-level",
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+        "page_idx": 3
+    },
+    {
+        "type": "page_number",
+        "text": "4",
+        "bbox": [
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+        "page_idx": 3
+    },
+    {
+        "type": "header",
+        "text": "J. Zhang, W. Huang, B. Peng, M. Wu, F. Hu, Z. Chen, B. Zhao, H. Dong",
+        "bbox": [
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+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "prriors for enhanced estimation. HS-Pose [44] extends 3D-GC [14] for improved feature extraction from point clouds. IST-Net [16] transforms features between camera and world spaces implicitly, without relying on 3D priors, surpassing previous methods. However, these techniques, mainly regression-based, require ad-hoc designs for symmetric objects. GenPose [43] addresses it by generatively modeling the pose distribution, eliminating the need for symmetry considerations. Yet, GenPose neglects RGB semantic information, which is increasingly vital as object category scales grow. Additionally, its energy-based aggregation algorithm fails with discontinuous multimodal distributions. We introduce GenPose++, which incorporates a 2D foundation model to leverage RGB semantics, improving generalization, and introduces an aggregation module to handle discrete multimodal distributions, addressing the limitations of GenPose.",
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+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "6D object pose tracking. This paper is situated within the domain of category-level 6D object pose tracking and model-free object tracking. BundleTrack [31] pioneers model-free tracking by leveraging multi-view feature detection for tracking unseen objects without 3D models. CAPTRA [32] enhances articulated pose tracking through recursive updates for better temporal consistency. CATRE [18] aligns partially observed point clouds to abstract shape priors for relative transformations and pose estimation. GenPose, adopting a generative approach, effectively addresses the challenge of pose ambiguities in symmetric objects. Together, these approaches underscore the evolving landscape of object pose tracking, highlighting both the progress and the diversity of strategies being explored.",
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+    {
+        "type": "text",
+        "text": "3 Omni6DPose Dataset",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "This paper introduces a rich variety of object categories, a large-scale, and materially diverse dataset for real 6D object pose estimation, named ROPE. And, a simulated dataset, SOPE, synthesizing with mixed reality and featuring depth simulation, is provided for training. Section 3.1 will discuss the collection and alignment of 3D objects. Section 3.2 will cover the acquisition and labeling of the ROPE dataset. Section 3.3 will explain the generation of the SOPE dataset.",
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+    {
+        "type": "text",
+        "text": "3.1 3D Object Collection and Alignment",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "Universal 6D object pose estimation relies on a comprehensive set of objects. We selected 149 categories of everyday objects, all reconstructed with high-precision scanners, and categorized them into two sets: SOPE for simulated data and ROPE for real-world scenes. SOPE primarily includes objects from sources like OmniObject3D [35], PhoCal [30], and GoogleScan [7], alongside a subset from our scans, totaling 5,000 instances. ROPE consists of 580 instances we reconstructed using industrial scanners. Importantly, while most SOPE objects are from public datasets, manual category-level pose alignment is necessary. For object reconstruction, as shown in Figure 3, we use two professional scanners, EinScan H2 $^{7}$  and Revopoint POP 3 $^{8}$  for objects in different scales. The scan",
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+        "page_idx": 4
+    },
+    {
+        "type": "header",
+        "text": "Omni6DPose",
+        "bbox": [
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+        "page_idx": 4
+    },
+    {
+        "type": "page_number",
+        "text": "5",
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+    },
+    {
+        "type": "page_footnote",
+        "text": "$^{7}$  https://www.einscan.com/",
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+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "page_footnote",
+        "text": "<sup>8</sup> https://www_REVopoint3d.com/",
+        "bbox": [
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+        "page_idx": 4
+    },
+    {
+        "type": "image",
+        "img_path": "images/ee72ddeb9dd614b8107981b5074fea0e9e9d96b84d2dc124a17f7cf47d4d842b.jpg",
+        "image_caption": [
+            "Fig. 3: ROPE dataset collection and annotation. (1) Object scanning, where high-precision industrial scanners are used to acquire the CAD models of objects; (2) Object canonicalization, involving the alignment of each object category to the canonical space; (3) Video capture, capturing video sequences in varied scenarios with a depth camera; and (4) Pose annotation, calculating camera poses through Structure from Motion (SFM), further utilizing Farthest Point Sampling (FPS) to select keyframes for keypoint annotation, and performing bundle adjustment to derive initial object pose values, which are then manually refined to obtain more precise annotations."
+        ],
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+        "page_idx": 5
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+    {
+        "type": "image",
+        "img_path": "images/fdeea2904a14f8872961b09ecaedf36c35b31eb8abc2fe5c48f6bd60ff54a8c1.jpg",
+        "image_caption": [],
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+    {
+        "type": "text",
+        "text": "ning time depends on object features: it took about 15 minutes for small, simple, Lambertian items like a mouse, and up to an hour for complex, large, or non-Lambertian items like a transparent mug. Finally, we constructed a specialized annotation tool for manually aligning objects in the same category to the category-level canonical space, with each alignment taking roughly one minute.",
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+    {
+        "type": "text",
+        "text": "3.2 ROPE Acquisition and Annotation",
+        "text_level": 1,
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "The ROPE dataset was systematically acquired utilizing the RealSense D415 imaging device, encompassing scenarios with 2 to 6 distinct objects and video sequences extending from 762 to 1,349 frames. The integrity and utility of the dataset are underpinned by the precision of object pose annotations, which present notable challenges, chiefly:",
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+        "list_items": [
+            "1. Derivation of relative camera poses, denoted as  $T_{c} = \\{(R_{i},t_{i})\\}_{i = 1}^{n}$ , where each pair  $(R_i,t_i)\\in \\mathrm{SE}(3)$ , signifying the transformation from the camera space to the world space for the  $i^{th}$  frame, with  $n$  symbolizing the aggregate frame count within the video sequence.",
+            "2. Procurement of high-accuracy object poses, represented as  $T_{o} = \\{(R,t)\\}$ , where the pair  $(R,t) \\in \\mathrm{SE}(3)$ , delineating the transformation from the object space the camera space for any selected frame."
+        ],
+        "bbox": [
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+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "With  $T_{c}$  and  $T_{o}$  known, it is possible to automate the generation of all annotations within the dataset. Addressing these challenges, we propose a marker-free annotation system. Previous datasets for calculating relative camera pose rely on markers, like NOCS [28], or external robot arms for indirect calculation, such as PhoCal [30], limiting scene diversity. As shown in Figure 3, to enable marker-free annotation in open scenes, we consider it a structure-from-motion (SfM) problem, utilizing intrinsic scene features to optimize camera poses  $T_{c}$  using bundle adjustment techniques. This approach aims to solve the optimization problem:",
+        "bbox": [
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+        "page_idx": 5
+    },
+    {
+        "type": "page_number",
+        "text": "6",
+        "bbox": [
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+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "header",
+        "text": "J. Zhang, W. Huang, B. Peng, M. Wu, F. Hu, Z. Chen, B. Zhao, H. Dong",
+        "bbox": [
+            269,
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+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "image",
+        "img_path": "images/ee6825a0c6cd87d43cf531eb473786b2d5b4fc4606b3b301b1180875f4d4c284.jpg",
+        "image_caption": [
+            "Fig. 4: SOPE synthesis, utilizing mixed reality to bridge the RGB sim2real gap and physical-based depth sensor simulation to minimize the geometric sim2real gap."
+        ],
+        "image_footnote": [],
+        "bbox": [
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+        "page_idx": 6
+    },
+    {
+        "type": "image",
+        "img_path": "images/b6208e949c4c1e03ff6fa2b7e8f0a535277ba4674ccd460c5d24793674b3bd92.jpg",
+        "image_caption": [],
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+    },
+    {
+        "type": "equation",
+        "text": "\n$$\n\\min  _ {T _ {c}} \\sum_ {i = 1} ^ {n} \\sum_ {j \\in \\mathcal {P} _ {i}} \\| \\pi \\left(R _ {i} X _ {j} + t _ {i}\\right) - x _ {i j} \\| ^ {2} \\tag {1}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
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+            303,
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+            344
+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "where  $\\pi$  denotes the camera projection function,  $X_{j}$  represents the 3D points in the world space, and  $x_{ij}$  corresponds to the 2D projection of  $X_{j}$  in the  $i^{th}$  camera frame, with  $\\mathcal{P}_i$  being the set of point correspondences in frame  $i$ .",
+        "bbox": [
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+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "For object pose  $T_{o}$  annotations, previous methods consider only a single frame, leading to inaccuracies due to the lack of multi-viewpoint constraints. To overcome this, we introduce a two-stage object pose annotation process: in the first stage, keyframes are sampled from SfM results using Farthest Point Sampling (FPS), and 2D-3D keypoint pairs on these keyframes are annotated, providing initial object pose by minimizing the reprojection error:",
+        "bbox": [
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+        "page_idx": 6
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\n\\min  _ {R, t} \\sum_ {k \\in \\mathcal {K}} \\| \\pi \\left(R X _ {k} + t\\right) - x _ {k} \\| ^ {2} \\tag {2}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
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+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "where  $X_{k}$  are 3D keypoints of object,  $x_{k}$  are corresponding 2D annotations in the image, and  $\\mathcal{K}$  is the set of all keypoint correspondences. The initial poses are manually fine-tuned based on the object's projection across all keyframes.",
+        "bbox": [
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+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "3.3 SOPE Synthesis",
+        "text_level": 1,
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "ROPE represents a comprehensive benchmark in category-level object pose estimation by scaling up the diversity and number of object categories to unprecedented levels, encompassing a wide range of materials. This diversity presents new challenges for network training data due to the higher demands on the dataset's scale and diversity. Collecting a larger real-world dataset would be prohibitively expensive and unlikely to ensure sufficient diversity.",
+        "bbox": [
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+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "To bridge the sim2real gap, which is pronounced when using synthetic data, either in RGB or geometry, this paper proposes a novel method based on mixed reality with depth simulation for synthetic data generation. Specifically, as demonstrated in Figure 4, we employ mixed reality [28] techniques to generate RGB data, thereby reducing the RGB sim2real gap. In parallel, we simulate the mechanism of structured light depth sensors within blender [6]. This involves rendering infrared (IR) images and applying stereo matching to produce synthetic depth maps, effectively narrowing the geometry's sim2real gap.",
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+        "page_idx": 6
+    },
+    {
+        "type": "header",
+        "text": "Omni6DPose",
+        "bbox": [
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+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "page_number",
+        "text": "7",
+        "bbox": [
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+        "page_idx": 6
+    },
+    {
+        "type": "image",
+        "img_path": "images/b24d7864e681bc47c2a299d2a477da0236290482111c0d79cb050ffe4dbd8173.jpg",
+        "image_caption": [
+            "Fig. 5: Omni6DPose statistics, showcasing the dataset distribution. Left: Category distribution, highlighting 149 categories and diverse materials. Right: Object size distribution across 5000 objects, illustrating diversity in shapes."
+        ],
+        "image_footnote": [],
+        "bbox": [
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+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "image",
+        "img_path": "images/a8960cd64b7036bd10197f1ff8903d7c074e3fd6859f78cfb271cb3f671c0fc7.jpg",
+        "image_caption": [],
+        "image_footnote": [],
+        "bbox": [
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+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "During data generation, we implement domain randomization for illumination and object materials to further enhance the dataset's diversity. All the background images are sourced from public datasets, including 19,658 images from MatterPort3D [2], 2,572 from ScanNet++ [38], and 540 from IKEA [28]. To the best of our knowledge, this is the first simulated dataset that uses a context-aware mixed reality approach combined with physical-based depth sensor simulation for object pose estimation tasks.",
+        "bbox": [
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+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "3.4 Dataset Statistics",
+        "text_level": 1,
+        "bbox": [
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+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "Object Category Statistics The comprehensive distribution of object category and size are both demonstrated in Figure 5. Most of the categories possess  $\\geq 25\\mathrm{K}$  pose annotations in the SOPE dataset, providing sufficient training opportunities. Categories containing objects with diverse and challenging material options (e.g., transparent or specular materials) are equipped with apparently more data generation, such as dishes, cups, bottles, bowls, mugs, etc.",
+        "bbox": [
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+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "Object Size Statistics As shown in Figure 5, the objects in our dataset span a wide range of sizes. The majority of the objects are approximately 0.1 meters in length along the diagonal of their bounding boxes, with the largest objects exceeding 1 meter.",
+        "bbox": [
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+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "4 Category-level 6D Pose Estimation Method",
+        "text_level": 1,
+        "bbox": [
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+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "Given the Omni6DPose, one naturally ponders the optimal technical approach for large-scale category-level pose estimation. The recently introduced state-of-the-art category-level 6D pose estimation technique, GenPose [43], offers a promising avenue by employing a diffusion-based probabilistic method [23, 24]. Moreover, the diffusion model has demonstrated remarkable efficacy across various high-dimensional domains with extensive training data [5, 25, 34, 42]. Expanding on this groundwork, our study delves further into the probabilistic approach, presenting an enhanced iteration of GenPose, named GenPose++.",
+        "bbox": [
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+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "page_number",
+        "text": "8",
+        "bbox": [
+            217,
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+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "header",
+        "text": "J. Zhang, W. Huang, B. Peng, M. Wu, F. Hu, Z. Chen, B. Zhao, H. Dong",
+        "bbox": [
+            269,
+            113,
+            761,
+            128
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "image",
+        "img_path": "images/bd6667f41f34f9d181debef2094ec10cadf3934d738b094a7bbef93d5a365440.jpg",
+        "image_caption": [
+            "Fig. 6: Overview of GenPose++. We employ segmented point clouds and cropped RGB images as inputs. PointNet++ extracts geometric features, while DINO v2, a pre-trained 2D foundation model, extracts semantic features. These features are fused to condition a diffusion model, which generates object pose candidates and their corresponding energy. Finally, clustering is applied to address aggregation issues in the multimodal distribution of poses for objects with non-continuous symmetry, such as boxes, effectively resolving the pose estimation challenge."
+        ],
+        "image_footnote": [],
+        "bbox": [
+            218,
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+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "$\\mathrm{GenPose + + }$  integrates two crucial enhancements: Semantic-aware feature extraction (see Fig.6 (a)) and Clustering-based aggregation (as shown in Fig.6 (c)). The subsequent sections will detail the three primary stages of the GenPose++ pipeline. Moreover, given the estimated 6D pose,  $\\mathrm{GenPose + + }$  provide a additional regression network to predict the 3D scale of the object.",
+        "bbox": [
+            214,
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+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "4.1 Training Semantic-aware Score and Energy Networks",
+        "text_level": 1,
+        "bbox": [
+            215,
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+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "The learning agent is trained on our paired dataset  $\\mathcal{D} = \\{(p_k,O_k,I_k)\\}_{k = 1}^n$ , where  $\\pmb {p}_k\\in \\mathrm{SE}(3)$ ,  $O_{k}\\in \\mathbb{R}^{3\\times N}$ , and  $I_{k}\\in \\mathbb{R}^{3\\times H\\times W}$  denote a 6D pose, a partially observed 3D point cloud with  $N$  points, and a cropped RGB image with  $H\\times W$  resolution, respectively. Given an unseen object with point cloud  $O^{*}$  and RGB image  $I^{*}$ , the goal is to recover the corresponding ground-truth pose  $\\pmb{p}^{*}$ .",
+        "bbox": [
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+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "Following GenPose, we initially train a score network  $\\varPhi_{\\theta}:\\mathbb{R}^{|\\mathcal{P}|}\\times \\mathbb{R}^{1}\\times \\mathbb{R}^{3\\times N}\\times$ $\\mathbb{R}^{3\\times H\\times W}\\to \\mathbb{R}^{|P|}$  and an energy network  $\\varPsi_{\\phi}:\\mathbb{R}^{|\\mathcal{P}|}\\times \\mathbb{R}^{1}\\times \\mathbb{R}^{3\\times N}\\times \\mathbb{R}^{3\\times H\\times W}\\to \\mathbb{R}^{1}$  from the dataset  $\\mathcal{D}$  using the denoising score-matching objective [26]:",
+        "bbox": [
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+        "page_idx": 8
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\n\\mathbb {E} _ {t \\sim \\mathcal {U} (\\epsilon , 1)} \\left\\{\\lambda (t) \\mathbb {E} _ {\\substack {\\boldsymbol {p} (0) \\sim p _ {\\text {data}} (\\boldsymbol {p} (0) | O, I), \\\\ \\boldsymbol {p} (t) \\sim \\mathcal {N} (\\boldsymbol {p} (t); \\boldsymbol {p} (0), \\sigma^ {2} (t) \\mathbf {I})}} \\left[ \\left\\| \\boldsymbol {s} (\\boldsymbol {p} (t), t | O, I) - \\frac {\\boldsymbol {p} (0) - \\boldsymbol {p} (t)}{\\sigma (t) ^ {2}} \\right\\| _ {2} ^ {2} \\right] \\right\\} \\tag{3}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
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+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "The training loss of the score and energy network can be obtained by replacing  $s(\\pmb{p}(t), t|O, I)$  in Eq. 3 with  $\\pmb{\\varPhi}_{\\theta}(\\pmb{p}(t), t|O, I)$  and  $\\nabla_{\\pmb{p}}\\pmb{\\Psi}_{\\phi}^{*}(\\pmb{p}(t), t|O, I)$ , respectively.",
+        "bbox": [
+            214,
+            728,
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+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "Unlike GenPose, our score and energy network are semantic-aware, as both networks are conditioned on an RGB image to incorporate semantic cues for pose estimation. To fuse the features extracted from the image and point cloud, we encode the RGB image img and point cloud obj using the pre-trained feature extractors from DINOv2 [20] and PointNet++ [22], respectively. Then, we concatenate these features together in a pointwise manner similar to [27].",
+        "bbox": [
+            212,
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+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "header",
+        "text": "Omni6DPose",
+        "bbox": [
+            642,
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+            126
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "page_number",
+        "text": "9",
+        "bbox": [
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+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "4.2 Candidates Generation and Outlier Removal",
+        "text_level": 1,
+        "bbox": [
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+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "Following GenPose, we subsequently sample pose candidates  $\\{\\hat{p}_i\\}_{i=1}^K$  by solving the following Probability Flow (PF) ODE [24] constructed by the score network  $\\Phi_\\theta$  from  $t = 1$  to  $t = \\epsilon$ :",
+        "bbox": [
+            212,
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+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\n\\frac {d \\boldsymbol {p}}{d t} = - \\sigma (t) \\dot {\\sigma} (t) \\boldsymbol {\\Phi} _ {\\theta} (\\boldsymbol {p} (t), t | O, I) \\tag {4}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
+            392,
+            220,
+            785,
+            247
+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "where  $\\pmb{p}(1) \\sim \\mathcal{N}(\\mathbf{0}, \\sigma_{\\max}^2 \\mathbf{I})$ ,  $\\sigma(t) = \\sigma_{\\min} \\left( \\frac{\\sigma_{\\max}}{\\sigma_{\\min}} \\right)^t$ ,  $\\sigma_{\\min} = 0.01$  and  $\\sigma_{\\max} = 50$ .",
+        "bbox": [
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+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "To remove the outliers in candidates, we sort the candidates into a sequence  $\\hat{\\pmb{p}}_{\\tau_1} \\succ \\hat{\\pmb{p}}_{\\tau_2} \\dots \\succ \\hat{\\pmb{p}}_{\\tau_K}$  where:",
+        "bbox": [
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+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\n\\hat {\\boldsymbol {p}} _ {\\tau_ {i}} \\succ \\hat {\\boldsymbol {p}} _ {\\tau_ {j}} \\Longleftrightarrow \\boldsymbol {\\Psi} _ {\\phi} (\\hat {\\boldsymbol {p}} _ {\\tau_ {i}}, \\epsilon | O) > \\boldsymbol {\\Psi} _ {\\phi} (\\hat {\\boldsymbol {p}} _ {\\tau_ {j}}, \\epsilon | O) \\tag {5}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
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+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "Then, we filter out the last  $1 - \\delta \\%$  candidates and obtain  $\\hat{\\pmb{p}}_{\\tau_1} \\succ \\hat{\\pmb{p}}_{\\tau_2} \\dots \\succ \\hat{\\pmb{p}}_{\\tau_M}$  where  $\\delta = 40\\%$  is a hyper parameter and  $M = \\lfloor \\delta \\cdot K \\rfloor$ .",
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "4.3 Clustering-based Aggregation",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "In this section, we aggregate the remaining candidates  $\\{\\hat{\\pmb{p}}_{\\tau_i} = (\\hat{T}_{\\tau_i},\\hat{R}_{\\tau_i})\\}_{i = 1}^M$  to obtain the final results. GenPose achieves this by simply mean-pooling the filtered candidates. However, this strategy will encounter a severe mean-mode issue when the object possesses discrete symmetrical properties. As illustrated in Fig. 6, a ping pong paddle has two symmetric ground truth poses (modes). Since the score network has encountered both modes during training, an optimally trained score network will likely output candidates around both modes. Simply mean-pooling these candidates will yield the average of the two modes, known as the 'mean mode', which will deviate from both modes.",
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+    {
+        "type": "text",
+        "text": "To mitigate this issue, we introduce a clustering-based aggregation mechanism. We employ DBSCAN [8] to cluster the candidates. It identify dense regions in the data space, forming clusters based on the density of data points and effectively separating noise from meaningful patterns, without the need to specify the number of clusters. This is achieved through dynamic determination of cluster quantities based on distance threshold  $(\\varepsilon)$  and density threshold (MinPts). For instance, in our empirical setting, we set  $\\varepsilon \\approx 0.45$  rad and MinPts  $= 5$ . After clustering the candidates, we select the cluster with the largest number of objects and get the mean-pooling result as the final estimation following GenPose.",
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+    {
+        "type": "text",
+        "text": "5 Experiments",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "5.1 Category-Level 6D Object Pose Estimation",
+        "text_level": 1,
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+        "type": "text",
+        "text": "Metric In prior studies, metrics such as the mean average precision (mAP) for 3D bounding box IoU and the mean average precision (mAP) for objects with translation errors less than  $m$  cm and rotation errors less than  $n^{\\circ}$  have been commonly used [28]. This evaluation typically involves two steps: instance-level object segmentation and pose estimation from these detections. However, these metrics are influenced by the detection model's performance. To focus solely on evaluating the precision of pose estimation, we assume ground truth instance segmentation is known and propose the following two metrics:",
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+    {
+        "type": "page_number",
+        "text": "10",
+        "bbox": [
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+    },
+    {
+        "type": "header",
+        "text": "J. Zhang, W. Huang, B. Peng, M. Wu, F. Hu, Z. Chen, B. Zhao, H. Dong",
+        "bbox": [
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+    },
+    {
+        "type": "table",
+        "img_path": "images/a7d38b6fc40da845b9125db99ea6d63455fa730cdeca388d79157a8f82bbf78b.jpg",
+        "table_caption": [
+            "Table 2: Quantitative comparison of category-level pose estimation on ROPE. \\(\\uparrow\\) represents a higher value indicating better performance, while \\(\\downarrow\\) represents a lower value indicating better performance. Prior-free indicates whether the method requires category prior. \\{'-'\\) indicates that GenPose does not predict the object scale."
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td rowspan=\"2\" colspan=\"2\">Method</td><td rowspan=\"2\">Input</td><td rowspan=\"2\">Prior-free</td><td colspan=\"3\">AUC ↑</td><td colspan=\"4\">VUS ↑</td></tr><tr><td>IoU25</td><td>IoU50</td><td>IoU75</td><td>5°2cm</td><td>5°5cm</td><td>10°2cm</td><td>10°5cm</td></tr><tr><td rowspan=\"4\">Deterministic</td><td>NOCS [28]</td><td>RGBD</td><td>✓</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr><tr><td>SGPA [3]</td><td>RGBD</td><td>✗</td><td>10.5</td><td>2.0</td><td>0.0</td><td>4.3</td><td>6.7</td><td>9.3</td><td>15.0</td></tr><tr><td>IST-Net [16]</td><td>RGBD</td><td>✓</td><td>28.7</td><td>10.6</td><td>0.5</td><td>2.0</td><td>3.4</td><td>5.3</td><td>8.8</td></tr><tr><td>HS-Pose [44]</td><td>D</td><td>✓</td><td>31.6</td><td>13.6</td><td>1.1</td><td>3.5</td><td>5.3</td><td>8.4</td><td>12.7</td></tr><tr><td rowspan=\"2\">Probabilistic</td><td>GenPose [43]</td><td>D</td><td>✓</td><td>-</td><td>-</td><td>-</td><td>6.6</td><td>9.6</td><td>13.1</td><td>19.3</td></tr><tr><td>GenPose++(Ours)</td><td>RGBD</td><td>✓</td><td>39.0</td><td>19.1</td><td>2.0</td><td>10.0</td><td>15.1</td><td>19.5</td><td>29.4</td></tr></table>",
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+    },
+    {
+        "type": "list",
+        "sub_type": "text",
+        "list_items": [
+            "- AUC@IoU $_n$ : This metric assesses the accuracy of predicted 3D bounding boxes, calculated via the Area Under the Curve (AUC) from various Intersection over Union (IoU) thresholds starting at  $n$ . In our study, we utilize AUC@IoU $_{25}$ , AUC@IoU $_{50}$ , and AUC@IoU $_{75}$  as the benchmarks.",
+            "- VUS@ $n^{\\circ}mcm$ : This metric offers a detailed analysis of 6D pose estimation accuracy, derived from the Volume Under Surface (VUS) across ranges of rotational (up to  $n^{\\circ}$ ) and translational (up to  $mcm$ ) error thresholds. VUS aggregates the accuracy of pose predictions within set boundaries. In this paper, we apply VUS@ $5^{\\circ}2cm$ , VUS@ $5^{\\circ}5cm$ , VUS@ $10^{\\circ}2cm$ , and VUS@ $10^{\\circ}5cm$  for comprehensive performance assessment."
+        ],
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+    {
+        "type": "text",
+        "text": "Additionally, to demonstrate the robustness of our method to segmentation masks, we present the pose estimation performance of GenPose++ using SAM [13] segmentation results in the supplementary material.",
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+    {
+        "type": "text",
+        "text": "Baselines We evaluate five category-level pose estimation methods: NOCS [28], SGPA [3], HS-Pose [44], IST-Net [16] and GenPose [43]. Except for NOCS, which conducts both object detection and pose estimation as a whole, all methods are equipped with ground truth detection results. For SGPA, the prior point cloud of each category is constructed by randomly selecting an object from the training dataset. Considering that previous methods' augmentations for symmetry properties are only applicable to specific object categories within the NOCS dataset and not suitable for all object categories in Omni6Dpose, data augmentation for object symmetry is disabled during training for all baseline methods. All methods are trained on SOPE and directly tested on ROPE.",
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+    {
+        "type": "text",
+        "text": "Results and Analysis. In Table 2, we present the quantitative evaluation results of previous methods compared to GenPose++ on the ROPE dataset. Overall, generative methods continue to dominate in the performance evaluation on ROPE. The VUS surface depicted in Figure 7 provides a more detailed reflection of the performance of each model. Unlike deterministic approaches, generative methods can handle ambiguity without any specialized design requirements. Moreover, these methods directly generate the distribution of object poses, eliminating the need for depth map-based pose fitting. This approach is particularly",
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+    {
+        "type": "header",
+        "text": "Omni6DPose",
+        "bbox": [
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+        "page_idx": 10
+    },
+    {
+        "type": "page_number",
+        "text": "11",
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+    {
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+        "img_path": "images/8577d558e9ccc200a47273e9044677f58ad2053039a13285e0607224c4d64cc9.jpg",
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+    {
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+    {
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+        "img_path": "images/fec35d4ed07e2909d968f20e09d0c1a7fd9731396d3a649319ce5e8374292dad.jpg",
+        "image_caption": [
+            "Fig. 7: Qualitative comparison with baselines on ROPE dataset."
+        ],
+        "image_footnote": [],
+        "bbox": [
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+    },
+    {
+        "type": "image",
+        "img_path": "images/03ce1c66280d01f8a3c7411edbf255b53f6b004a790450d12fe12ac5dee565f6.jpg",
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+    {
+        "type": "image",
+        "img_path": "images/2525cd83110824542c6fb1a551052ff2447259b4ec7dd52b968d177337a488e9.jpg",
+        "image_caption": [],
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+    {
+        "type": "text",
+        "text": "advantageous for challenging material types, such as transparent or reflective objects, where structured-light depth cameras tend to introduce significant noise, severely impacting pose fitting accuracy. Furthermore, the NOCS method does not demonstrate effective performance on the ROPE dataset, leading to the supposition that methods relying solely on RGB information to predict the shape of an object in the canonical space become less robust as the scale of category diversity increases. Compared to GenPose, GenPose++ achieves a significant lead by leveraging the powerful perception capabilities of the 2D foundation model, along with the robustness of clustering towards discrete symmetric properties. You can find qualitative visualizations in Figure 8.",
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "5.2 Ablation Study",
+        "text_level": 1,
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "In order to validate the design decisions of our approach, we performed a series of ablation experiments on our method:",
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+    },
+    {
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+        "sub_type": "text",
+        "list_items": [
+            "- w/o clustering. Without clustering directly take the average of all remaining pose candidates after outlier removal as the pose estimation output.",
+            "- w/o scale prediction. Use the estimated pose to transform the observed point cloud into object space, then take the bounding box length as the maximum projection from the point cloud to each axis.",
+            "- w/o simulated depth. Use perfect depth for training.",
+            "- w/o point-wise feature fusion. In the feature extraction stage, separately extract the RGB feature and geometric feature, and then concatenation."
+        ],
+        "bbox": [
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+        "page_idx": 11
+    },
+    {
+        "type": "text",
+        "text": "Table 3 illustrates the contribution of each component of GenPose++ to its performance. The introduction of the clustering module allows GenPose++ to effectively aggregate the multimodal distributions caused by discrete symmetries, leading to higher performance. The scale prediction in GenPose++ significantly surpasses direct calculations from the object's point cloud due to ambiguities from partial observations and errors from point cloud noise, particularly in transparent and reflective objects. Training with simulated depth data results in",
+        "bbox": [
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+        "page_idx": 11
+    },
+    {
+        "type": "page_number",
+        "text": "12",
+        "bbox": [
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+        ],
+        "page_idx": 11
+    },
+    {
+        "type": "header",
+        "text": "J. Zhang, W. Huang, B. Peng, M. Wu, F. Hu, Z. Chen, B. Zhao, H. Dong",
+        "bbox": [
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+        "page_idx": 11
+    },
+    {
+        "type": "table",
+        "img_path": "images/3afd96b96af6e85dcbc6fd6490af7edbf579f44495b34ee8436c5e695e886d30.jpg",
+        "table_caption": [
+            "Table 3: Ablation study on category-level 6D object pose estimation"
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Ablation</td><td colspan=\"3\">AUC ↑</td><td colspan=\"4\">VUS ↑</td></tr><tr><td></td><td>IoU25</td><td>IoU50</td><td>IoU75</td><td>5°2cm</td><td>5°5cm</td><td>10°2cm</td><td>10°5cm</td></tr><tr><td>Full</td><td>39.0</td><td>19.1</td><td>2.0</td><td>10.0</td><td>15.1</td><td>19.5</td><td>29.4</td></tr><tr><td>w/o scale prediction</td><td>13.8</td><td>3.4</td><td>0.2</td><td>10.0</td><td>15.1</td><td>19.5</td><td>29.4</td></tr><tr><td>w/o clustering</td><td>38.6</td><td>18.7</td><td>1.9</td><td>9.4</td><td>14.1</td><td>18.4</td><td>27.8</td></tr><tr><td>w/o simulated depth</td><td>35.3</td><td>16.5</td><td>1.7</td><td>7.3</td><td>11.9</td><td>14.6</td><td>24.3</td></tr><tr><td>w/o point-wise fusion</td><td>34.2</td><td>15.4</td><td>1.4</td><td>7.1</td><td>10.6</td><td>14.6</td><td>21.9</td></tr></table>",
+        "bbox": [
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+        "page_idx": 12
+    },
+    {
+        "type": "image",
+        "img_path": "images/0adc22a2ab6a498e57403c9ae654072fee93fcf84548c0e1cdf8b1a97d1e38dd.jpg",
+        "image_caption": [
+            "Fig. 8: Qualitative comparison with baselines on ROPE dataset."
+        ],
+        "image_footnote": [],
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "better performance than training with perfect point clouds, as the physics-based depth camera simulation substantially reduces the sim2real gap for depth data. Point-wise fusion outperforms global fusion as it retains more of the object's local geometric features, which are crucial for accurate object pose prediction.",
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "5.3 Category-Level 6D Object Pose Tracking",
+        "text_level": 1,
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "Metric. We report the following metrics for object pose tracking evaluation:",
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+    },
+    {
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+        "sub_type": "text",
+        "list_items": [
+            "- FPS: Frames Per Second, which indicates the speed of pose tracking.",
+            "- VUS@5°5cm: Volume Under Surface, assessing pose estimation accuracy for rotation errors within 0 to  $5^{\\circ}$  and translation errors within 0 to 5cm.",
+            "- mIoU: Mean Intersection over Union, representing the average 3D overlap between the ground truth and the predicted bounding boxes.",
+            "-  $\\mathbf{Rerr}(\\circ)$ : Average rotation error in degrees.",
+            "-  $\\mathbf{Terr}(\\mathbf{cm})$ : Average translation error in centimeters."
+        ],
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "Baselines. This paper employs BundleTrack, CATRE, and GenPose as baselines for object pose tracking. BundleTrack is a training-free approach that utilizes multi-view feature point detection and matching for tracking the pose of unseen objects. CATRE aligns partially observed point clouds to abstract shape priors to estimate relative transformations, enhancing pose accuracy. Conversely, GenPose utilizes a generative approach to effectively resolve pose ambiguities, notably in symmetric objects. Following GenPose, we have adapted GenPose++ to object pose tracking with minor modifications.",
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+        "page_idx": 12
+    },
+    {
+        "type": "header",
+        "text": "Omni6DPose",
+        "bbox": [
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+        "page_idx": 12
+    },
+    {
+        "type": "page_number",
+        "text": "13",
+        "bbox": [
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+        "page_idx": 12
+    },
+    {
+        "type": "text",
+        "text": "Results and Analysis. Table 4 presents the results of category-level object pose tracking algorithms CATRE, GenPose, and our method, as well as the results for the unseen object pose tracking algorithm BundleTrack. The category-level pose estimation methods appear to achieve relatively better outcomes since they benefit from learning the category-level canonical space of objects within the SOPE dataset. However, for the training-free BundleTrack, reliance on RGB information for keypoint detection and matching poses challenges, often failing on objects with weak textures. Additionally, its dependency on depth values for global optimization renders it less effective in handling instances with substantial depth noise, such as transparent or specular objects. Our method, without undergoing specialized design, has achieved results comparable to state-of-the-art approaches. Although the inference speed of our method is lower than that of CATRE and GenPose, the achieved 17.8 FPS is sufficient for certain downstream tasks, such as robotic manipulation. Furthermore, recent rapid developments in research on fast samplers are beneficial to our approach, potentially enhancing its performance and applicability in real-time scenarios.",
+        "bbox": [
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+        ],
+        "page_idx": 13
+    },
+    {
+        "type": "table",
+        "img_path": "images/f634bc31b432a88ca62eefc05fecd81ccbed471dff7b48076fe4609e385c2e08.jpg",
+        "table_caption": [
+            "Table 4: Results of category-level object pose tracking on ROPE. The results are averaged over all 149 categories. GT. Pert. denotes that a perturbed ground truth pose is utilized as the initial object pose."
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Methods</td><td>Input</td><td>Init.</td><td>Speed(FPS)↑</td><td>5°5cm↑</td><td>mIoU↑</td><td>Rerr(°)↓</td><td>Terr(cm)↓</td></tr><tr><td>BundleTrack [31]</td><td>RGBD</td><td>GT.</td><td>12.4</td><td>1.3</td><td>3.9</td><td>46.9</td><td>23.5</td></tr><tr><td>CATRE [18]</td><td>D</td><td>GT. Pert.</td><td>38.5</td><td>15.9</td><td>55.4</td><td>21.3</td><td>2.6</td></tr><tr><td>GenPose [43]</td><td>D</td><td>GT. Pert.</td><td>26.3</td><td>13.3</td><td>-</td><td>19.3</td><td>1.2</td></tr><tr><td>GenPose++(Ours)</td><td>RGBD</td><td>GT. Pert.</td><td>17.8</td><td>15.9</td><td>53.4</td><td>17.6</td><td>1.2</td></tr></table>",
+        "bbox": [
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+        "page_idx": 13
+    },
+    {
+        "type": "text",
+        "text": "6 Conclusions and Discussion",
+        "text_level": 1,
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "In this study, we introduce Omni6DPose, a comprehensive dataset for 6D object pose estimation, featuring extensive scale, diversity, and material variety. Through thorough experimentation, our findings suggest that the probabilistic framework holds promise for category-level 6D object estimation, leveraging semantic information provided by RGB images to address large-scale pose estimation challenges. However, the performance of GenPose++ on Omni6DPose reveals significant room for improvement, with the model still hampered by slow inference speeds resulting from the iterative refinement nature inherent in the diffusion model. Future works could focus on addressing these challenges and integrating the universal 6D pose estimation module trained on Omni6DPose into a broader range of downstream tasks [4,33,37,42].",
+        "bbox": [
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+        "page_idx": 13
+    },
+    {
+        "type": "text",
+        "text": "7 Acknowledgment",
+        "text_level": 1,
+        "bbox": [
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+        "page_idx": 13
+    },
+    {
+        "type": "text",
+        "text": "This work is supported by the National Natural Science Foundation of China - General Program (Project ID: 62376006), the National Youth Talent Support Program (Project ID: 8200800081), and NSFC-62306046.",
+        "bbox": [
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+    },
+    {
+        "type": "page_number",
+        "text": "14",
+        "bbox": [
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+        ],
+        "page_idx": 13
+    },
+    {
+        "type": "header",
+        "text": "J. Zhang, W. Huang, B. Peng, M. Wu, F. Hu, Z. Chen, B. Zhao, H. Dong",
+        "bbox": [
+            269,
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2024/Omni6DPose_ A Benchmark and Model for Universal 6D Object Pose Estimation and Tracking/cf5501e2-725b-4f12-bc02-1c29948756c3_origin.pdf

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2024/Omni6DPose_ A Benchmark and Model for Universal 6D Object Pose Estimation and Tracking/full.md

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+# Omni6DPose: A Benchmark and Model for Universal 6D Object Pose Estimation and Tracking
+
+Jiyao Zhang $^{1,2,3*}$ , Weiyao Huang $^{1*}$ , Bo Peng $^{1*}$ , Mingdong Wu $^{1,2,3}$ , Fei Hu $^{1,2,3}$ , Zijian Chen $^{4}$ , Bo Zhao $^{5,6}$ , and Hao Dong $^{1,2,3\dagger}$
+
+$^{1}$  Center on Frontiers of Computing Studies, School of Computer Science, Peking University  $^{2}$  PKU-Agibot Lab, School of Computer Science, Peking University  
+$^{3}$  National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University  $^{4}$  Waseda University  $^{5}$  Shanghai Jiao Tong University  $^{6}$  BAAI {jiyaozhang, sshwy, bo.peng, hofee}@stu.pku.edu.cn zijianchenwaseda@akane.waseda.jp bo.zhao@sjtu.edu.cn {wmingd, hao.dong}@pku.edu.cn
+
+https://jiyao06.github.io/Omni6DPose/
+
+Abstract. 6D object pose estimation is crucial in the field of computer vision. However, it suffers from a significant lack of large-scale and diverse datasets, impeding comprehensive model evaluation and curtailing downstream applications. To address these issues, this paper introduces Omni6DPose, a substantial benchmark featured by its diversity in object categories, large scale, and variety in object materials. Omni6DPose is divided into three main components: ROPE (Real 6D Object Pose Estimation Dataset), which includes 332K images annotated with over 1.5M annotations across 581 instances in 149 categories; SOPE(Simulated 6D Object Pose Estimation Dataset), a simulated training set created by mixed reality and physics-based depth simulation; and PAM(Pose Aligned 3D Models), the manually aligned real scanned objects used in ROPE and SOPE. Omni6DPose is inherently challenging due to the substantial variations and ambiguities. To address this issue, we introduce GenPose++, an enhanced version of the SOTA category-level 6D object pose estimation framework, incorporating two pivotal improvements: Semantic-aware feature extraction and Clustering-based aggregation. Moreover, we provide a comprehensive benchmarking analysis to evaluate the performance of previous methods on this new large-scale dataset in the realms of 6D object pose estimation and pose tracking.
+
+Keywords: Benchmark  $\cdot$  object pose estimation  $\cdot$  object pose tracking
+
+# 1 Introduction
+
+6D object pose estimation [16,28,45] and pose tracking [15,21] from single images is an essential task in computer vision, holding immense potential for applications in robotics [1] and augmented reality/virtual reality (AR/VR) [19]. Over recent decades, the domain has experienced significant advancements, primarily dominated by data-driven learning approaches. Analogous to the pivotal role of data in learning-based 2D foundation tasks, high-quality, comprehensive datasets are paramount in the context of 6D object pose estimation and tracking.
+
+![](images/6e30f9fb1ef9101e65707ac7f168841fdf416464f146a4f76a392dff6c0d76ea.jpg)  
+Fig. 1: We introduce Omni6DPose for universal 6D object pose estimation and tracking, which comprises three key components: (1) ROPE (right), a large-scale real-world dataset for evaluation. (2) SOPE (left), a vast synthetic dataset generated through mixed reality and physics-based simulation for training. (3) PAM (center), a collection of pose-aligned 3D object models. Omni6DPose is featured by its diversity in object categories, large scale, and variety in object materials.
+
+Today 6D object pose estimation is studied under two lenses: instance-level and category-level. In instance-level settings, datasets such as Linemod [10], YCB-Video [36], and T-LESS [11] have gained widespread acceptance as benchmarks. These datasets are distinguished by their focus on detailed, individual object instances, thereby enabling algorithms to precisely learn and predict the poses of specific items. On the other hand, category-level pose estimation emphasizes generalization across different items within a particular object category. The NOCS [28] dataset stands out as the most widely used in the category-level object pose estimation field, providing a simulated dataset for training and a small-scale real-world dataset for evaluation. Despite their contributions to advancing the field, these datasets present limitations due to their small scale in terms of instances or categories. This results in two significant challenges:
+
+1. It hampers comprehensive evaluation of different models' performance, limiting the development of research in this field.  
+2. It restricts the applicability of research findings across diverse domains, due to the limited variety of object instances or categories represented.
+
+To address the aforementioned challenges and drive advancements in this field, this paper introduces Omni6DPose, a universal 6D object pose estimation dataset featured by its diversity in object categories, large scale, and variety in object materials. Omni6DPose is segmented into three principal components: (1) ROPE (Real 6D Object Pose Estimation Dataset), which encompasses  $332\mathrm{K}$  images annotated with over 1.5M annotations across 581 instances in 149 categories; (2) SOPE (Simulated 6D Object Pose Estimation Dataset), comprising  $475\mathrm{K}$  images generated in a mixed reality setting with depth simulation, furnished with over 5M annotations spanning 4162 instances in the same 149
+
+![](images/1a60111597c270923e5a9fe2d9337ff4c25a52767811667ea1190b636c2b590f.jpg)
+
+![](images/e30d062643149c774e69ec1aee782198766bc6e4587e0098a9fa42d0a9e1cac6.jpg)
+
+![](images/43275bc3b8beb29183c70ae36d9adae17d40d064a09a49bb097a67b6f133b781.jpg)  
+Fig. 2: ROPE dataset visualization. In the figure, bounding boxes are colored according to the coordinates in the object's coordinate system.
+
+![](images/44ff07bbb3552f46ea54d5355b86627c6238da0b82a64dc8ea16bf8033463a1d.jpg)
+
+![](images/6468a5f50a56759c8b1bbf03e1aa1b915eb71628c75cdcf150bdc344d9cccb97.jpg)
+
+![](images/57fea1551a53410447966c9d8b602271c459fccd8967a553a84fabb67a4623ed.jpg)
+
+![](images/b8808cb50f283d7c2f4884717cd265866e36cf74011e315d21131af851534fce.jpg)
+
+![](images/172fd19562b74c51f5ab7fa2c961c534695d45e685e2ea5611bb5ebf4f21b840.jpg)
+
+![](images/5b423517e481a3550e627b5e8ada15cd45c58163646cad585cb6f5cab3e6e0f8.jpg)
+
+![](images/cfc55b1d4839de861b119a85310341fb2d3146b2b9b25b746da1388d41fadb75.jpg)
+
+![](images/2996098182c76da3d4a14b8593baa467e1c4312c27a59b8b6e9fdb2fbb194d5d.jpg)
+
+![](images/f89108a76ee285ad48ac784e5c47e30101301b38ef746d7f5687b12476d37d37.jpg)
+
+![](images/9b4943baaa6d025c9201974d559431e8719b9631d0841cc21f6dd49a6c98717b.jpg)
+
+![](images/7511dc9f5d88aada149176f9573656101fa178c98e0e1b8986d4678451f1b6cf.jpg)
+
+![](images/5eeb4a4d92a5496ce3e870202d7eb7a7e2a8f2723c6af499c5d3209a846479de.jpg)
+
+categories. The mixed reality bridges the semantic sim2real gap, while the depth sensor simulation closes the geometric sim2real gap; (3) PAM(Pose Aligned 3D Models), the manually aligned, real scanned objects utilized in both ROPE and SOPE, enabling the generation of diverse downstream task data.
+
+Omni6DPose poses inherent challenges due to its considerable variations, diverse materials, and inherent ambiguities, which reflect the complexities encountered in real-world applications. Figure 2 illustrates some examples from ROPE. To tackle these issues, we introduce GenPose++, which incorporates GenPose [43] with two pivotal improvements: Semantic-aware feature extraction and Clustering-based aggregation, tailored specifically to the nuances of the Omni6DPose in question. Furthermore, as a Universal 6D object pose estimation dataset, this paper also offers a comprehensive benchmarking analysis to assess the performance of existing methods on category-level 6D object pose estimation and pose tracking. We summarize our contributions as follows:
+
+1. We present Omni6DPose, a comprehensive 6D object pose estimation dataset with extensive categories, instance diversity, and material variety.  
+2. We propose a real data collection pipeline and a simulation framework for generating synthetic data with low semantic and geometry sim2real gaps.  
+3. We introduce GenPose++ for category-level 6D object pose estimation and tracking, demonstrating SOTA performance on Omni6DPose.
+
+# 2 Related work
+
+For 6D object pose estimation, there are two main branches: instance-level and category-level. Instance-level estimation is tested on seen objects or on unseen objects with reference, while category-level estimation is tested on unseen instances of known categories without CAD models. In this section, we review and compare existing datasets to our large-scale category-level dataset and review relevant algorithms for category-level pose estimation and tracking.
+
+# 2.1 6D Object Pose Estimation Datasets
+
+Following the outlined branches of 6D object pose estimation, we have reviewed datasets corresponding to both instance-level and category-level 6D object pose estimation. A comparative analysis of these datasets is provided in Table 1.
+
+Table 1: This table compares datasets for 6D object pose estimation, focusing on object category count, reality of the data, data modalities (RGB, Depth, IR), and object attributes such as quantity, CAD model availability, and inclusion of transparent and specular objects. It also details video characteristics by number and marker presence, along with image and annotation counts. 'Wild6D' refers specifically to the test split of the Wild6D dataset, as the training data does not provide annotations. The symbol '-' indicates the absence of a particular feature within the dataset.  
+
+<table><tr><td rowspan="2">Dataset</td><td rowspan="2" colspan="2">Cat. Real</td><td colspan="3">Modality</td><td colspan="4">Object</td><td rowspan="2">Marker-free Vid.</td><td rowspan="2">Img.</td><td rowspan="2">Anno.</td></tr><tr><td>RGB</td><td>Depth IR</td><td>IR</td><td colspan="4">Num. CAD Trans. Spec.</td></tr><tr><td>CAMERA [28]</td><td>6</td><td>X</td><td>✓</td><td>✓</td><td>X</td><td>1085</td><td>✓</td><td>X</td><td>X</td><td>✓</td><td>-</td><td>300K</td></tr><tr><td>SOPE(Ours)</td><td>149</td><td>X</td><td>✓</td><td>✓</td><td>✓</td><td>4162</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>-</td><td>475K</td></tr><tr><td>YCB-Video [36]</td><td>-</td><td>✓</td><td>✓</td><td>✓</td><td>X</td><td>21</td><td>✓</td><td>X</td><td>X</td><td>✓</td><td>92</td><td>133K</td></tr><tr><td>T-LESS [11]</td><td>-</td><td>✓</td><td>✓</td><td>✓</td><td>X</td><td>30</td><td>✓</td><td>X</td><td>X</td><td>X</td><td>20</td><td>48K</td></tr><tr><td>Linemod [10]</td><td>-</td><td>✓</td><td>✓</td><td>✓</td><td>X</td><td>15</td><td>✓</td><td>X</td><td>X</td><td>X</td><td>-</td><td>18K</td></tr><tr><td>StereoOBJ-1M [17]</td><td>-</td><td>✓</td><td>✓</td><td>X</td><td>X</td><td>18</td><td>✓</td><td>✓</td><td>✓</td><td>X</td><td>182</td><td>393K</td></tr><tr><td>REAL275 [28]</td><td>6</td><td>✓</td><td>✓</td><td>✓</td><td>X</td><td>42</td><td>✓</td><td>X</td><td>X</td><td>X</td><td>18</td><td>8K</td></tr><tr><td>PhoCal [30]</td><td>8</td><td>✓</td><td>✓</td><td>✓</td><td>X</td><td>60</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>24</td><td>3.9K</td></tr><tr><td>HouseCat6D [12]</td><td>10</td><td>✓</td><td>✓</td><td>✓</td><td>X</td><td>194</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>41</td><td>23.5K</td></tr><tr><td>Wild6D* [9]</td><td>5</td><td>✓</td><td>✓</td><td>✓</td><td>X</td><td>162</td><td>X</td><td>(✓)</td><td>X</td><td>✓</td><td>486</td><td>10K</td></tr><tr><td>PACE [41]</td><td>44</td><td>✓</td><td>✓</td><td>✓</td><td>X</td><td>576</td><td>✓</td><td>X</td><td>X</td><td>✓</td><td>300</td><td>55K</td></tr><tr><td>ROPE(Ours)</td><td>149</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>581</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>363</td><td>332K</td></tr></table>
+
+Instance-level 6D object pose estimation dataset. LineMod [10] is one of the most used datasets, providing non-temporal RGB-D images and ground truth pose annotations. YCB-Video [36] provides RGB-D videos and annotations, enabling pose-tracking approaches. T-LESS [11] features texture-less objects with symmetries and mutual similarities. StereoOBJ-1M [17] achieves a leap in dataset scale and features transparent and reflective objects. While these datasets are extensive in terms of image and annotation count, they are limited in the diversity of instances they cover. For example, StereoOBJ-1M comprises 339K frames and 1.5M annotations, yet it includes only 18 unique object instances.
+
+Category-level 6D object pose estimation dataset. NOCS [28] provides the first benchmark in category-level pose estimation, while Wild6D [9] addresses the scalability issue of datasets by leveraging unlabeled and synthetic data. PhoCal [30] focuses on photometrically challenging objects and HouseCat6D [12] offers diverse scenes, viewpoints, and grasping annotations. However, these datasets cover only a limited number of categories, even the most extensive dataset, HouseCat6D, includes merely ten categories. PACE [41], a concurrent work, focusing on object pose estimation in complex scenes. In contrast, our datasets, SOPE and ROPE, set new benchmarks by offering the widest range of categories and featuring objects with diverse materials, thereby enhancing dataset diversity and realism for pose estimation research.
+
+# 2.2 6D Object Pose Estimation and Tracking Algorithm
+
+Category-level 6D object pose estimation. Category-level 6D object pose estimation [28,39,40] aims to estimate unseen instance poses within the same category. NOCS [28] introduces a normalized object coordinate space for pose prediction without CAD models, while SPD [29] and SGPA [3] utilize category-level
+
+prriors for enhanced estimation. HS-Pose [44] extends 3D-GC [14] for improved feature extraction from point clouds. IST-Net [16] transforms features between camera and world spaces implicitly, without relying on 3D priors, surpassing previous methods. However, these techniques, mainly regression-based, require ad-hoc designs for symmetric objects. GenPose [43] addresses it by generatively modeling the pose distribution, eliminating the need for symmetry considerations. Yet, GenPose neglects RGB semantic information, which is increasingly vital as object category scales grow. Additionally, its energy-based aggregation algorithm fails with discontinuous multimodal distributions. We introduce GenPose++, which incorporates a 2D foundation model to leverage RGB semantics, improving generalization, and introduces an aggregation module to handle discrete multimodal distributions, addressing the limitations of GenPose.
+
+6D object pose tracking. This paper is situated within the domain of category-level 6D object pose tracking and model-free object tracking. BundleTrack [31] pioneers model-free tracking by leveraging multi-view feature detection for tracking unseen objects without 3D models. CAPTRA [32] enhances articulated pose tracking through recursive updates for better temporal consistency. CATRE [18] aligns partially observed point clouds to abstract shape priors for relative transformations and pose estimation. GenPose, adopting a generative approach, effectively addresses the challenge of pose ambiguities in symmetric objects. Together, these approaches underscore the evolving landscape of object pose tracking, highlighting both the progress and the diversity of strategies being explored.
+
+# 3 Omni6DPose Dataset
+
+This paper introduces a rich variety of object categories, a large-scale, and materially diverse dataset for real 6D object pose estimation, named ROPE. And, a simulated dataset, SOPE, synthesizing with mixed reality and featuring depth simulation, is provided for training. Section 3.1 will discuss the collection and alignment of 3D objects. Section 3.2 will cover the acquisition and labeling of the ROPE dataset. Section 3.3 will explain the generation of the SOPE dataset.
+
+# 3.1 3D Object Collection and Alignment
+
+Universal 6D object pose estimation relies on a comprehensive set of objects. We selected 149 categories of everyday objects, all reconstructed with high-precision scanners, and categorized them into two sets: SOPE for simulated data and ROPE for real-world scenes. SOPE primarily includes objects from sources like OmniObject3D [35], PhoCal [30], and GoogleScan [7], alongside a subset from our scans, totaling 5,000 instances. ROPE consists of 580 instances we reconstructed using industrial scanners. Importantly, while most SOPE objects are from public datasets, manual category-level pose alignment is necessary. For object reconstruction, as shown in Figure 3, we use two professional scanners, EinScan H2 $^{7}$  and Revopoint POP 3 $^{8}$  for objects in different scales. The scan
+
+![](images/ee72ddeb9dd614b8107981b5074fea0e9e9d96b84d2dc124a17f7cf47d4d842b.jpg)  
+Fig. 3: ROPE dataset collection and annotation. (1) Object scanning, where high-precision industrial scanners are used to acquire the CAD models of objects; (2) Object canonicalization, involving the alignment of each object category to the canonical space; (3) Video capture, capturing video sequences in varied scenarios with a depth camera; and (4) Pose annotation, calculating camera poses through Structure from Motion (SFM), further utilizing Farthest Point Sampling (FPS) to select keyframes for keypoint annotation, and performing bundle adjustment to derive initial object pose values, which are then manually refined to obtain more precise annotations.
+
+![](images/fdeea2904a14f8872961b09ecaedf36c35b31eb8abc2fe5c48f6bd60ff54a8c1.jpg)
+
+ning time depends on object features: it took about 15 minutes for small, simple, Lambertian items like a mouse, and up to an hour for complex, large, or non-Lambertian items like a transparent mug. Finally, we constructed a specialized annotation tool for manually aligning objects in the same category to the category-level canonical space, with each alignment taking roughly one minute.
+
+# 3.2 ROPE Acquisition and Annotation
+
+The ROPE dataset was systematically acquired utilizing the RealSense D415 imaging device, encompassing scenarios with 2 to 6 distinct objects and video sequences extending from 762 to 1,349 frames. The integrity and utility of the dataset are underpinned by the precision of object pose annotations, which present notable challenges, chiefly:
+
+1. Derivation of relative camera poses, denoted as  $T_{c} = \{(R_{i},t_{i})\}_{i = 1}^{n}$ , where each pair  $(R_i,t_i)\in \mathrm{SE}(3)$ , signifying the transformation from the camera space to the world space for the  $i^{th}$  frame, with  $n$  symbolizing the aggregate frame count within the video sequence.  
+2. Procurement of high-accuracy object poses, represented as  $T_{o} = \{(R,t)\}$ , where the pair  $(R,t) \in \mathrm{SE}(3)$ , delineating the transformation from the object space the camera space for any selected frame.
+
+With  $T_{c}$  and  $T_{o}$  known, it is possible to automate the generation of all annotations within the dataset. Addressing these challenges, we propose a marker-free annotation system. Previous datasets for calculating relative camera pose rely on markers, like NOCS [28], or external robot arms for indirect calculation, such as PhoCal [30], limiting scene diversity. As shown in Figure 3, to enable marker-free annotation in open scenes, we consider it a structure-from-motion (SfM) problem, utilizing intrinsic scene features to optimize camera poses  $T_{c}$  using bundle adjustment techniques. This approach aims to solve the optimization problem:
+
+![](images/ee6825a0c6cd87d43cf531eb473786b2d5b4fc4606b3b301b1180875f4d4c284.jpg)  
+Fig. 4: SOPE synthesis, utilizing mixed reality to bridge the RGB sim2real gap and physical-based depth sensor simulation to minimize the geometric sim2real gap.
+
+![](images/b6208e949c4c1e03ff6fa2b7e8f0a535277ba4674ccd460c5d24793674b3bd92.jpg)
+
+$$
+\min  _ {T _ {c}} \sum_ {i = 1} ^ {n} \sum_ {j \in \mathcal {P} _ {i}} \| \pi \left(R _ {i} X _ {j} + t _ {i}\right) - x _ {i j} \| ^ {2} \tag {1}
+$$
+
+where  $\pi$  denotes the camera projection function,  $X_{j}$  represents the 3D points in the world space, and  $x_{ij}$  corresponds to the 2D projection of  $X_{j}$  in the  $i^{th}$  camera frame, with  $\mathcal{P}_i$  being the set of point correspondences in frame  $i$ .
+
+For object pose  $T_{o}$  annotations, previous methods consider only a single frame, leading to inaccuracies due to the lack of multi-viewpoint constraints. To overcome this, we introduce a two-stage object pose annotation process: in the first stage, keyframes are sampled from SfM results using Farthest Point Sampling (FPS), and 2D-3D keypoint pairs on these keyframes are annotated, providing initial object pose by minimizing the reprojection error:
+
+$$
+\min  _ {R, t} \sum_ {k \in \mathcal {K}} \| \pi \left(R X _ {k} + t\right) - x _ {k} \| ^ {2} \tag {2}
+$$
+
+where  $X_{k}$  are 3D keypoints of object,  $x_{k}$  are corresponding 2D annotations in the image, and  $\mathcal{K}$  is the set of all keypoint correspondences. The initial poses are manually fine-tuned based on the object's projection across all keyframes.
+
+# 3.3 SOPE Synthesis
+
+ROPE represents a comprehensive benchmark in category-level object pose estimation by scaling up the diversity and number of object categories to unprecedented levels, encompassing a wide range of materials. This diversity presents new challenges for network training data due to the higher demands on the dataset's scale and diversity. Collecting a larger real-world dataset would be prohibitively expensive and unlikely to ensure sufficient diversity.
+
+To bridge the sim2real gap, which is pronounced when using synthetic data, either in RGB or geometry, this paper proposes a novel method based on mixed reality with depth simulation for synthetic data generation. Specifically, as demonstrated in Figure 4, we employ mixed reality [28] techniques to generate RGB data, thereby reducing the RGB sim2real gap. In parallel, we simulate the mechanism of structured light depth sensors within blender [6]. This involves rendering infrared (IR) images and applying stereo matching to produce synthetic depth maps, effectively narrowing the geometry's sim2real gap.
+
+![](images/b24d7864e681bc47c2a299d2a477da0236290482111c0d79cb050ffe4dbd8173.jpg)  
+Fig. 5: Omni6DPose statistics, showcasing the dataset distribution. Left: Category distribution, highlighting 149 categories and diverse materials. Right: Object size distribution across 5000 objects, illustrating diversity in shapes.
+
+![](images/a8960cd64b7036bd10197f1ff8903d7c074e3fd6859f78cfb271cb3f671c0fc7.jpg)
+
+During data generation, we implement domain randomization for illumination and object materials to further enhance the dataset's diversity. All the background images are sourced from public datasets, including 19,658 images from MatterPort3D [2], 2,572 from ScanNet++ [38], and 540 from IKEA [28]. To the best of our knowledge, this is the first simulated dataset that uses a context-aware mixed reality approach combined with physical-based depth sensor simulation for object pose estimation tasks.
+
+# 3.4 Dataset Statistics
+
+Object Category Statistics The comprehensive distribution of object category and size are both demonstrated in Figure 5. Most of the categories possess  $\geq 25\mathrm{K}$  pose annotations in the SOPE dataset, providing sufficient training opportunities. Categories containing objects with diverse and challenging material options (e.g., transparent or specular materials) are equipped with apparently more data generation, such as dishes, cups, bottles, bowls, mugs, etc.
+
+Object Size Statistics As shown in Figure 5, the objects in our dataset span a wide range of sizes. The majority of the objects are approximately 0.1 meters in length along the diagonal of their bounding boxes, with the largest objects exceeding 1 meter.
+
+# 4 Category-level 6D Pose Estimation Method
+
+Given the Omni6DPose, one naturally ponders the optimal technical approach for large-scale category-level pose estimation. The recently introduced state-of-the-art category-level 6D pose estimation technique, GenPose [43], offers a promising avenue by employing a diffusion-based probabilistic method [23, 24]. Moreover, the diffusion model has demonstrated remarkable efficacy across various high-dimensional domains with extensive training data [5, 25, 34, 42]. Expanding on this groundwork, our study delves further into the probabilistic approach, presenting an enhanced iteration of GenPose, named GenPose++.
+
+![](images/bd6667f41f34f9d181debef2094ec10cadf3934d738b094a7bbef93d5a365440.jpg)  
+Fig. 6: Overview of GenPose++. We employ segmented point clouds and cropped RGB images as inputs. PointNet++ extracts geometric features, while DINO v2, a pre-trained 2D foundation model, extracts semantic features. These features are fused to condition a diffusion model, which generates object pose candidates and their corresponding energy. Finally, clustering is applied to address aggregation issues in the multimodal distribution of poses for objects with non-continuous symmetry, such as boxes, effectively resolving the pose estimation challenge.
+
+$\mathrm{GenPose + + }$  integrates two crucial enhancements: Semantic-aware feature extraction (see Fig.6 (a)) and Clustering-based aggregation (as shown in Fig.6 (c)). The subsequent sections will detail the three primary stages of the GenPose++ pipeline. Moreover, given the estimated 6D pose,  $\mathrm{GenPose + + }$  provide a additional regression network to predict the 3D scale of the object.
+
+# 4.1 Training Semantic-aware Score and Energy Networks
+
+The learning agent is trained on our paired dataset  $\mathcal{D} = \{(p_k,O_k,I_k)\}_{k = 1}^n$ , where  $\pmb {p}_k\in \mathrm{SE}(3)$ ,  $O_{k}\in \mathbb{R}^{3\times N}$ , and  $I_{k}\in \mathbb{R}^{3\times H\times W}$  denote a 6D pose, a partially observed 3D point cloud with  $N$  points, and a cropped RGB image with  $H\times W$  resolution, respectively. Given an unseen object with point cloud  $O^{*}$  and RGB image  $I^{*}$ , the goal is to recover the corresponding ground-truth pose  $\pmb{p}^{*}$ .
+
+Following GenPose, we initially train a score network  $\varPhi_{\theta}:\mathbb{R}^{|\mathcal{P}|}\times \mathbb{R}^{1}\times \mathbb{R}^{3\times N}\times$ $\mathbb{R}^{3\times H\times W}\to \mathbb{R}^{|P|}$  and an energy network  $\varPsi_{\phi}:\mathbb{R}^{|\mathcal{P}|}\times \mathbb{R}^{1}\times \mathbb{R}^{3\times N}\times \mathbb{R}^{3\times H\times W}\to \mathbb{R}^{1}$  from the dataset  $\mathcal{D}$  using the denoising score-matching objective [26]:
+
+$$
+\mathbb {E} _ {t \sim \mathcal {U} (\epsilon , 1)} \left\{\lambda (t) \mathbb {E} _ {\substack {\boldsymbol {p} (0) \sim p _ {\text {data}} (\boldsymbol {p} (0) | O, I), \\ \boldsymbol {p} (t) \sim \mathcal {N} (\boldsymbol {p} (t); \boldsymbol {p} (0), \sigma^ {2} (t) \mathbf {I})}} \left[ \left\| \boldsymbol {s} (\boldsymbol {p} (t), t | O, I) - \frac {\boldsymbol {p} (0) - \boldsymbol {p} (t)}{\sigma (t) ^ {2}} \right\| _ {2} ^ {2} \right] \right\} \tag{3}
+$$
+
+The training loss of the score and energy network can be obtained by replacing  $s(\pmb{p}(t), t|O, I)$  in Eq. 3 with  $\pmb{\varPhi}_{\theta}(\pmb{p}(t), t|O, I)$  and  $\nabla_{\pmb{p}}\pmb{\Psi}_{\phi}^{*}(\pmb{p}(t), t|O, I)$ , respectively.
+
+Unlike GenPose, our score and energy network are semantic-aware, as both networks are conditioned on an RGB image to incorporate semantic cues for pose estimation. To fuse the features extracted from the image and point cloud, we encode the RGB image img and point cloud obj using the pre-trained feature extractors from DINOv2 [20] and PointNet++ [22], respectively. Then, we concatenate these features together in a pointwise manner similar to [27].
+
+# 4.2 Candidates Generation and Outlier Removal
+
+Following GenPose, we subsequently sample pose candidates  $\{\hat{p}_i\}_{i=1}^K$  by solving the following Probability Flow (PF) ODE [24] constructed by the score network  $\Phi_\theta$  from  $t = 1$  to  $t = \epsilon$ :
+
+$$
+\frac {d \boldsymbol {p}}{d t} = - \sigma (t) \dot {\sigma} (t) \boldsymbol {\Phi} _ {\theta} (\boldsymbol {p} (t), t | O, I) \tag {4}
+$$
+
+where  $\pmb{p}(1) \sim \mathcal{N}(\mathbf{0}, \sigma_{\max}^2 \mathbf{I})$ ,  $\sigma(t) = \sigma_{\min} \left( \frac{\sigma_{\max}}{\sigma_{\min}} \right)^t$ ,  $\sigma_{\min} = 0.01$  and  $\sigma_{\max} = 50$ .
+
+To remove the outliers in candidates, we sort the candidates into a sequence  $\hat{\pmb{p}}_{\tau_1} \succ \hat{\pmb{p}}_{\tau_2} \dots \succ \hat{\pmb{p}}_{\tau_K}$  where:
+
+$$
+\hat {\boldsymbol {p}} _ {\tau_ {i}} \succ \hat {\boldsymbol {p}} _ {\tau_ {j}} \Longleftrightarrow \boldsymbol {\Psi} _ {\phi} (\hat {\boldsymbol {p}} _ {\tau_ {i}}, \epsilon | O) > \boldsymbol {\Psi} _ {\phi} (\hat {\boldsymbol {p}} _ {\tau_ {j}}, \epsilon | O) \tag {5}
+$$
+
+Then, we filter out the last  $1 - \delta \%$  candidates and obtain  $\hat{\pmb{p}}_{\tau_1} \succ \hat{\pmb{p}}_{\tau_2} \dots \succ \hat{\pmb{p}}_{\tau_M}$  where  $\delta = 40\%$  is a hyper parameter and  $M = \lfloor \delta \cdot K \rfloor$ .
+
+# 4.3 Clustering-based Aggregation
+
+In this section, we aggregate the remaining candidates  $\{\hat{\pmb{p}}_{\tau_i} = (\hat{T}_{\tau_i},\hat{R}_{\tau_i})\}_{i = 1}^M$  to obtain the final results. GenPose achieves this by simply mean-pooling the filtered candidates. However, this strategy will encounter a severe mean-mode issue when the object possesses discrete symmetrical properties. As illustrated in Fig. 6, a ping pong paddle has two symmetric ground truth poses (modes). Since the score network has encountered both modes during training, an optimally trained score network will likely output candidates around both modes. Simply mean-pooling these candidates will yield the average of the two modes, known as the 'mean mode', which will deviate from both modes.
+
+To mitigate this issue, we introduce a clustering-based aggregation mechanism. We employ DBSCAN [8] to cluster the candidates. It identify dense regions in the data space, forming clusters based on the density of data points and effectively separating noise from meaningful patterns, without the need to specify the number of clusters. This is achieved through dynamic determination of cluster quantities based on distance threshold  $(\varepsilon)$  and density threshold (MinPts). For instance, in our empirical setting, we set  $\varepsilon \approx 0.45$  rad and MinPts  $= 5$ . After clustering the candidates, we select the cluster with the largest number of objects and get the mean-pooling result as the final estimation following GenPose.
+
+# 5 Experiments
+
+# 5.1 Category-Level 6D Object Pose Estimation
+
+Metric In prior studies, metrics such as the mean average precision (mAP) for 3D bounding box IoU and the mean average precision (mAP) for objects with translation errors less than  $m$  cm and rotation errors less than  $n^{\circ}$  have been commonly used [28]. This evaluation typically involves two steps: instance-level object segmentation and pose estimation from these detections. However, these metrics are influenced by the detection model's performance. To focus solely on evaluating the precision of pose estimation, we assume ground truth instance segmentation is known and propose the following two metrics:
+
+Table 2: Quantitative comparison of category-level pose estimation on ROPE. \(\uparrow\) represents a higher value indicating better performance, while \(\downarrow\) represents a lower value indicating better performance. Prior-free indicates whether the method requires category prior. \{'-'\) indicates that GenPose does not predict the object scale.  
+
+<table><tr><td rowspan="2" colspan="2">Method</td><td rowspan="2">Input</td><td rowspan="2">Prior-free</td><td colspan="3">AUC ↑</td><td colspan="4">VUS ↑</td></tr><tr><td>IoU25</td><td>IoU50</td><td>IoU75</td><td>5°2cm</td><td>5°5cm</td><td>10°2cm</td><td>10°5cm</td></tr><tr><td rowspan="4">Deterministic</td><td>NOCS [28]</td><td>RGBD</td><td>✓</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr><tr><td>SGPA [3]</td><td>RGBD</td><td>✗</td><td>10.5</td><td>2.0</td><td>0.0</td><td>4.3</td><td>6.7</td><td>9.3</td><td>15.0</td></tr><tr><td>IST-Net [16]</td><td>RGBD</td><td>✓</td><td>28.7</td><td>10.6</td><td>0.5</td><td>2.0</td><td>3.4</td><td>5.3</td><td>8.8</td></tr><tr><td>HS-Pose [44]</td><td>D</td><td>✓</td><td>31.6</td><td>13.6</td><td>1.1</td><td>3.5</td><td>5.3</td><td>8.4</td><td>12.7</td></tr><tr><td rowspan="2">Probabilistic</td><td>GenPose [43]</td><td>D</td><td>✓</td><td>-</td><td>-</td><td>-</td><td>6.6</td><td>9.6</td><td>13.1</td><td>19.3</td></tr><tr><td>GenPose++(Ours)</td><td>RGBD</td><td>✓</td><td>39.0</td><td>19.1</td><td>2.0</td><td>10.0</td><td>15.1</td><td>19.5</td><td>29.4</td></tr></table>
+
+- AUC@IoU $_n$ : This metric assesses the accuracy of predicted 3D bounding boxes, calculated via the Area Under the Curve (AUC) from various Intersection over Union (IoU) thresholds starting at  $n$ . In our study, we utilize AUC@IoU $_{25}$ , AUC@IoU $_{50}$ , and AUC@IoU $_{75}$  as the benchmarks.  
+- VUS@ $n^{\circ}mcm$ : This metric offers a detailed analysis of 6D pose estimation accuracy, derived from the Volume Under Surface (VUS) across ranges of rotational (up to  $n^{\circ}$ ) and translational (up to  $mcm$ ) error thresholds. VUS aggregates the accuracy of pose predictions within set boundaries. In this paper, we apply VUS@ $5^{\circ}2cm$ , VUS@ $5^{\circ}5cm$ , VUS@ $10^{\circ}2cm$ , and VUS@ $10^{\circ}5cm$  for comprehensive performance assessment.
+
+Additionally, to demonstrate the robustness of our method to segmentation masks, we present the pose estimation performance of GenPose++ using SAM [13] segmentation results in the supplementary material.
+
+Baselines We evaluate five category-level pose estimation methods: NOCS [28], SGPA [3], HS-Pose [44], IST-Net [16] and GenPose [43]. Except for NOCS, which conducts both object detection and pose estimation as a whole, all methods are equipped with ground truth detection results. For SGPA, the prior point cloud of each category is constructed by randomly selecting an object from the training dataset. Considering that previous methods' augmentations for symmetry properties are only applicable to specific object categories within the NOCS dataset and not suitable for all object categories in Omni6Dpose, data augmentation for object symmetry is disabled during training for all baseline methods. All methods are trained on SOPE and directly tested on ROPE.
+
+Results and Analysis. In Table 2, we present the quantitative evaluation results of previous methods compared to GenPose++ on the ROPE dataset. Overall, generative methods continue to dominate in the performance evaluation on ROPE. The VUS surface depicted in Figure 7 provides a more detailed reflection of the performance of each model. Unlike deterministic approaches, generative methods can handle ambiguity without any specialized design requirements. Moreover, these methods directly generate the distribution of object poses, eliminating the need for depth map-based pose fitting. This approach is particularly
+
+![](images/8577d558e9ccc200a47273e9044677f58ad2053039a13285e0607224c4d64cc9.jpg)
+
+![](images/7b2051dcc5293d9a7fe0796028210d31c06e74c9b9890724cda7329cb842a01e.jpg)
+
+![](images/c077af05b498de02cdc8eab0c596a7d8c21efe75484d6d0273e29d17bcfd9d10.jpg)
+
+![](images/fec35d4ed07e2909d968f20e09d0c1a7fd9731396d3a649319ce5e8374292dad.jpg)  
+Fig. 7: Qualitative comparison with baselines on ROPE dataset.
+
+![](images/03ce1c66280d01f8a3c7411edbf255b53f6b004a790450d12fe12ac5dee565f6.jpg)
+
+![](images/2525cd83110824542c6fb1a551052ff2447259b4ec7dd52b968d177337a488e9.jpg)
+
+advantageous for challenging material types, such as transparent or reflective objects, where structured-light depth cameras tend to introduce significant noise, severely impacting pose fitting accuracy. Furthermore, the NOCS method does not demonstrate effective performance on the ROPE dataset, leading to the supposition that methods relying solely on RGB information to predict the shape of an object in the canonical space become less robust as the scale of category diversity increases. Compared to GenPose, GenPose++ achieves a significant lead by leveraging the powerful perception capabilities of the 2D foundation model, along with the robustness of clustering towards discrete symmetric properties. You can find qualitative visualizations in Figure 8.
+
+# 5.2 Ablation Study
+
+In order to validate the design decisions of our approach, we performed a series of ablation experiments on our method:
+
+- w/o clustering. Without clustering directly take the average of all remaining pose candidates after outlier removal as the pose estimation output.  
+- w/o scale prediction. Use the estimated pose to transform the observed point cloud into object space, then take the bounding box length as the maximum projection from the point cloud to each axis.  
+- w/o simulated depth. Use perfect depth for training.  
+- w/o point-wise feature fusion. In the feature extraction stage, separately extract the RGB feature and geometric feature, and then concatenation.
+
+Table 3 illustrates the contribution of each component of GenPose++ to its performance. The introduction of the clustering module allows GenPose++ to effectively aggregate the multimodal distributions caused by discrete symmetries, leading to higher performance. The scale prediction in GenPose++ significantly surpasses direct calculations from the object's point cloud due to ambiguities from partial observations and errors from point cloud noise, particularly in transparent and reflective objects. Training with simulated depth data results in
+
+Table 3: Ablation study on category-level 6D object pose estimation  
+
+<table><tr><td>Ablation</td><td colspan="3">AUC ↑</td><td colspan="4">VUS ↑</td></tr><tr><td></td><td>IoU25</td><td>IoU50</td><td>IoU75</td><td>5°2cm</td><td>5°5cm</td><td>10°2cm</td><td>10°5cm</td></tr><tr><td>Full</td><td>39.0</td><td>19.1</td><td>2.0</td><td>10.0</td><td>15.1</td><td>19.5</td><td>29.4</td></tr><tr><td>w/o scale prediction</td><td>13.8</td><td>3.4</td><td>0.2</td><td>10.0</td><td>15.1</td><td>19.5</td><td>29.4</td></tr><tr><td>w/o clustering</td><td>38.6</td><td>18.7</td><td>1.9</td><td>9.4</td><td>14.1</td><td>18.4</td><td>27.8</td></tr><tr><td>w/o simulated depth</td><td>35.3</td><td>16.5</td><td>1.7</td><td>7.3</td><td>11.9</td><td>14.6</td><td>24.3</td></tr><tr><td>w/o point-wise fusion</td><td>34.2</td><td>15.4</td><td>1.4</td><td>7.1</td><td>10.6</td><td>14.6</td><td>21.9</td></tr></table>
+
+![](images/0adc22a2ab6a498e57403c9ae654072fee93fcf84548c0e1cdf8b1a97d1e38dd.jpg)  
+Fig. 8: Qualitative comparison with baselines on ROPE dataset.
+
+better performance than training with perfect point clouds, as the physics-based depth camera simulation substantially reduces the sim2real gap for depth data. Point-wise fusion outperforms global fusion as it retains more of the object's local geometric features, which are crucial for accurate object pose prediction.
+
+# 5.3 Category-Level 6D Object Pose Tracking
+
+Metric. We report the following metrics for object pose tracking evaluation:
+
+- FPS: Frames Per Second, which indicates the speed of pose tracking.  
+- VUS@5°5cm: Volume Under Surface, assessing pose estimation accuracy for rotation errors within 0 to  $5^{\circ}$  and translation errors within 0 to 5cm.  
+- mIoU: Mean Intersection over Union, representing the average 3D overlap between the ground truth and the predicted bounding boxes.  
+-  $\mathbf{Rerr}(\circ)$ : Average rotation error in degrees.  
+-  $\mathbf{Terr}(\mathbf{cm})$ : Average translation error in centimeters.
+
+Baselines. This paper employs BundleTrack, CATRE, and GenPose as baselines for object pose tracking. BundleTrack is a training-free approach that utilizes multi-view feature point detection and matching for tracking the pose of unseen objects. CATRE aligns partially observed point clouds to abstract shape priors to estimate relative transformations, enhancing pose accuracy. Conversely, GenPose utilizes a generative approach to effectively resolve pose ambiguities, notably in symmetric objects. Following GenPose, we have adapted GenPose++ to object pose tracking with minor modifications.
+
+Results and Analysis. Table 4 presents the results of category-level object pose tracking algorithms CATRE, GenPose, and our method, as well as the results for the unseen object pose tracking algorithm BundleTrack. The category-level pose estimation methods appear to achieve relatively better outcomes since they benefit from learning the category-level canonical space of objects within the SOPE dataset. However, for the training-free BundleTrack, reliance on RGB information for keypoint detection and matching poses challenges, often failing on objects with weak textures. Additionally, its dependency on depth values for global optimization renders it less effective in handling instances with substantial depth noise, such as transparent or specular objects. Our method, without undergoing specialized design, has achieved results comparable to state-of-the-art approaches. Although the inference speed of our method is lower than that of CATRE and GenPose, the achieved 17.8 FPS is sufficient for certain downstream tasks, such as robotic manipulation. Furthermore, recent rapid developments in research on fast samplers are beneficial to our approach, potentially enhancing its performance and applicability in real-time scenarios.
+
+Table 4: Results of category-level object pose tracking on ROPE. The results are averaged over all 149 categories. GT. Pert. denotes that a perturbed ground truth pose is utilized as the initial object pose.  
+
+<table><tr><td>Methods</td><td>Input</td><td>Init.</td><td>Speed(FPS)↑</td><td>5°5cm↑</td><td>mIoU↑</td><td>Rerr(°)↓</td><td>Terr(cm)↓</td></tr><tr><td>BundleTrack [31]</td><td>RGBD</td><td>GT.</td><td>12.4</td><td>1.3</td><td>3.9</td><td>46.9</td><td>23.5</td></tr><tr><td>CATRE [18]</td><td>D</td><td>GT. Pert.</td><td>38.5</td><td>15.9</td><td>55.4</td><td>21.3</td><td>2.6</td></tr><tr><td>GenPose [43]</td><td>D</td><td>GT. Pert.</td><td>26.3</td><td>13.3</td><td>-</td><td>19.3</td><td>1.2</td></tr><tr><td>GenPose++(Ours)</td><td>RGBD</td><td>GT. Pert.</td><td>17.8</td><td>15.9</td><td>53.4</td><td>17.6</td><td>1.2</td></tr></table>
+
+# 6 Conclusions and Discussion
+
+In this study, we introduce Omni6DPose, a comprehensive dataset for 6D object pose estimation, featuring extensive scale, diversity, and material variety. Through thorough experimentation, our findings suggest that the probabilistic framework holds promise for category-level 6D object estimation, leveraging semantic information provided by RGB images to address large-scale pose estimation challenges. However, the performance of GenPose++ on Omni6DPose reveals significant room for improvement, with the model still hampered by slow inference speeds resulting from the iterative refinement nature inherent in the diffusion model. Future works could focus on addressing these challenges and integrating the universal 6D pose estimation module trained on Omni6DPose into a broader range of downstream tasks [4,33,37,42].
+
+# 7 Acknowledgment
+
+This work is supported by the National Natural Science Foundation of China - General Program (Project ID: 62376006), the National Youth Talent Support Program (Project ID: 8200800081), and NSFC-62306046.
+
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+        "text": "Omni6D: Large-Vocabulary 3D Object Dataset for Category-Level 6D Object Pose Estimation",
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+        "text": "Mengchen Zhang $^{1,2}$ , Tong Wu $^{3}$ , Tai Wang $^{2}$ , Tengfei Wang $^{2}$ , Ziwei Liu $^{4}$ , and Dahua Lin $^{2,3}$",
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+        "text": "<sup>1</sup> Zhejiang University, Zhejiang, China",
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+        "type": "text",
+        "text": "{zhangmengchen, wangtai, wangtengfei}@pjlab.org.cn,",
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+        "text": "{wt020,dhlin}@ie.cuhk.edu.hk,ziwei.liu@ntu.edu.sg",
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+            "Fig.1: Omni6D is a dataset for 6D object pose and size estimation with large vocabulary categories and rich annotations. (a) showcases ground truth of RGB image, depth map and NOCS map. (b) presents shape priors derived from a variational autoencoder [5] with adjusted canonical poses. (c) provides examples of the rotational symmetry of objects we have annotated, indicating the multiples of angles by which the shape remains unchanged when rotated around the xyz axes."
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+        "text": "Abstract. 6D object pose estimation aims at determining an object's translation, rotation, and scale, typically from a single RGBD image. Recent advancements have expanded this estimation from instance-level to category-level, allowing models to generalize across unseen instances within the same category. However, this generalization is limited by the narrow range of categories covered by existing datasets, such as NOCS, which also tend to overlook common real-world challenges like occlusion. To tackle these challenges, we introduce Omni6D, a comprehensive RGBD dataset featuring a wide range of categories and varied backgrounds, elevating the task to a more realistic context. 1) The dataset comprises an extensive spectrum of 166 categories, 4688 instances adjusted to the canonical pose, and over 0.8 million captures, significantly broadening the scope for evaluation. 2) We introduce a symmetry-aware",
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+        "text": "metric and conduct systematic benchmarks of existing algorithms on Omni6D, offering a thorough exploration of new challenges and insights. 3) Additionally, we propose an effective fine-tuning approach that adapts models from previous datasets to our extensive vocabulary setting. We believe this initiative will pave the way for new insights and substantial progress in both the industrial and academic fields, pushing forward the boundaries of general 6D pose estimation.",
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+        "text": "Keywords: 6DoF Pose Estimation  $\\cdot$  Large Vocabulary Dataset  $\\cdot$  Metrics and Benchmarks",
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+    {
+        "type": "text",
+        "text": "1 Introduction",
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+        "text": "6D pose estimation aims at predicting the position, orientation, and size of objects in a 3D space using RGB (D) images, enabling various applications such as augmented/virtual reality [26,33], robot manipulation [11,35], and scene understanding [15,28].",
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+        "text": "Early instance-level pose estimation approaches [32, 38, 39, 42, 43] typically involve providing instance CAD models and predicting poses of instances that were seen during training, restricting the generalization to unseen objects. In contrast, recent research has shifted towards category-level 6D object pose estimation [6-8, 10, 16, 17, 20, 24, 25, 29, 34, 37, 40, 44-47], which learns category prior from a large number of instances within a category, allowing for pose estimation of new instances within the same category without the need for CAD models. By learning on a diverse range of categories, category-level approaches could be a more versatile solution for 6D pose estimation in real-world scenarios.",
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+        "text": "However, most existing datasets [22, 40, 44] are limited to a small number of object categories, typically less than 10, as shown in Tab. 1, hindering their practical applicability to complex scenes.",
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+        "text": "To overcome the limitations in previous category-level 6D pose estimation datasets, such as limited category numbers, lack of instance diversity within categories, and overly simplistic scenes, this paper presents a novel category-level dataset dubbed Omni6D for 6D pose estimation. Omni6D significantly extends the number of object categories to 166, and includes 4,688 real-scanned and well-annotated instance objects with a diverse range of shapes, sizes, and textures. The constructed benchmark includes 0.8M images featuring complex scenes with various occlusions, changing lighting conditions, complex backgrounds, and varying viewpoints. For each scene, we provide the rendered image, depth map, NOCS map, and instance mask. Also, considering the widespread rotational symmetry in objects, we examine three types of rotational invariance where an object maintains its original shape under following rotations: any degrees (Sym-1), multiples of 90 degrees (Sym-2) and 180 degrees (Sym-3). Additionally, we introduce a symmetry-aware metric to specifically address rotational invariance. Every object in Omni6D is adjusted to the canonical pose and annotated with rotational symmetry around three axes.",
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+        "page_idx": 1
+    },
+    {
+        "type": "header",
+        "text": "M. Zhang et al.",
+        "bbox": [
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+        ],
+        "page_idx": 1
+    },
+    {
+        "type": "table",
+        "img_path": "images/c956b8a1bf6ec64f44009d2c61c9b4f21b46619d7aea3acb8888e6b17a4644e6.jpg",
+        "table_caption": [
+            "Table 1: Comparisons between Omni6D and existing datasets. Omni6D significantly extends the range of everyday object categories and instances."
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Datasets</td><td>Mode</td><td>Realism</td><td># Categories # Instances</td><td># Images</td></tr><tr><td>ShapeNet-SRNCars [22]</td><td>RGB</td><td>Synthetic</td><td>1</td><td>3514</td></tr><tr><td>Sim2Real Cars [22]</td><td>RGB</td><td>Real</td><td>1</td><td>10</td></tr><tr><td>CAMERA [40]</td><td>RGBD</td><td>Synthetic</td><td>6</td><td>1085</td></tr><tr><td>REAL [40]</td><td>RGBD</td><td>Real</td><td>6</td><td>42</td></tr><tr><td>Wild6D [44]</td><td>RGBD</td><td>Real</td><td>5</td><td>1722</td></tr><tr><td>Omni6D</td><td>RGBD</td><td>Real-Scanned</td><td>166</td><td>4,688</td></tr></table>",
+        "bbox": [
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+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "Including a broader range of categories, our dataset offers a more comprehensive and challenging evaluation benchmark for category-level 6D object pose estimation. Utilizing Omni6D, we train and analyze existing algorithms, initiating a profound exploration of the challenges and vital elements involved in category-level estimation within large-vocabulary categories. Additionally, we assess these algorithms' capability to generalize across categories, and carry out a category-wise analysis. Experiments show that our dataset presents a more challenging benchmark for 6D pose estimation, highlighting the need for more robust and generalized pose estimation approaches. As an initial attempt, we present a finetuning strategy that assists in broadening the scope of existing approaches from a limited range of categories to a broader vocabulary. Moreover, we conduct an analysis of the domain gap between our dataset and real-world dataset, emphasizing the benefits of their combined use.",
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+    {
+        "type": "text",
+        "text": "Our dataset will be publicly available to the research community, which will foster future research on more practical and robust 6D pose estimation algorithms and pave the way for broader applications.",
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+    {
+        "type": "text",
+        "text": "2 Related Work",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "Existing work on category-level 6D object pose estimation can be generally divided into two types. After extracting features from images or point clouds, they compute Rotation, Translation, and Size (RTS) either through implicit point correspondence or explicit regression.",
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+    {
+        "type": "text",
+        "text": "Existing Datasets. The most commonly used dataset for category-level 6D object pose estimation is NOCS [40], comprising both the synthetic CAMERA dataset and the real-world REAL dataset. CAMERA includes 300k RGBD images of 31 indoor scenes with 1,085 object instances across 6 categories, while REAL mirrors the categories in CAMERA and includes 8k RGBD images capturing 42 instances in 18 real scenes. Wild6D [44] consists of 5,166 videos with 1.1 million images over 1,722 object instances in 5 categories. ShapeNet-SRN Cars dataset and Sim2Real Cars dataset proposed in iNerf [22] both exclusively include a single car category. The former includes 3,514 instances derived from ShapeNet cars, while the latter is extracted from videos capturing 10 distinct unseen car models. These datasets are limited by their narrow range of categories, hindering their ability to generalize broadly. Additionally, most training images",
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+    {
+        "type": "header",
+        "text": "Omni6D",
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+    },
+    {
+        "type": "page_number",
+        "text": "3",
+        "bbox": [
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+        ],
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+    },
+    {
+        "type": "text",
+        "text": "are synthetic and lack realism, and their scenes are overly simplified, failing to account for common real-world challenges like occlusions.",
+        "bbox": [
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+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "Implicit Methods. Implicit methods are based on point correspondence [6, 20, 24, 34, 37, 40, 44, 46]. NOCS [40], one of the pioneering works in this area, introduced the concept of Normalized Object Coordinate Space (NOCS). The final pose and size of the object are obtained by matching the predicted NOCS map with the observed depth input using the Umeyama algorithm [36] and RANSAC algorithm [12].",
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+    },
+    {
+        "type": "text",
+        "text": "Subsequent algorithms such as DualPoseNet, RBP-Net and RePoNet [20,44, 46] have continued to develop along the vein of NOCS, implicitly solving for pose after predicting the NOCS map. SPD [34] proposed a category-level shape prior, subsequently deforming this shape prior (i.e., average shape) to fit observed point cloud. SGPA, RePoNet, and CATRE [6,24,44] continue to develop along SPD's category-level shape prior approach. Algorithms like 6-PACK and SGPA [6,37] extract low-rank structure points, i.e., keypoints, from dense observed point clouds. 6-PACK [37] predicts interframe motion of target instances through keypoint matching, while SGPA [6] employs keypoints for more effective incorporation of sparse structural information during prior adaptation. These methods rely heavily on the RANSAC process to eliminate outliers, making them non-differentiable and time-consuming.",
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+    {
+        "type": "text",
+        "text": "Explicit Methods. Explicit methods are based on direct pose regression [7, 10, 20, 24, 46, 47]. DualPoseNet and RBP-Net [20, 46] conduct both explicit and implicit training, where one parallel pose decoder explicitly regresses the pose. CATRE [24], recognizes the inherent difference between estimations of rotation and translation/size, explicitly regressing their residuals and carrying out an iterative pose estimation process. FS-Net [7] designs an autoencoder with 3D Graphic Convolution for latent feature extraction and separates the predictions for rotation and translation/size into two distinct networks: one estimates translation/size through two residuals, while the other handles rotation prediction by estimating deflections on two orthogonal axes. GPV-Pose and HS-Pose [10, 47] utilize the same foundational mechanism introduced by FS-Net [7]. GPV-Pose [10] proposes a decoupled confidence-driven rotation representation that facilitates geometrically-aware recovery of correlated rotation matrices and introduces a new geometry-guided point-by-point voting paradigm for robust retrieval of 3D object bounding boxes. Meanwhile, HS-Pose [47] extends 3D-GC to extract mixed-range latent features from point cloud data through a simple network structure known as the HS layer.",
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+    {
+        "type": "text",
+        "text": "3 Omni6D Dataset",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "3.1 Construction",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "Dataset Collection. As shown in Tab. 1, Omni6D comprises 4,688 instances across an impressive span of 166 categories. Each instance is a high-resolution",
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+        "type": "page_number",
+        "text": "4",
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+    {
+        "type": "header",
+        "text": "M. Zhang et al.",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "textured mesh, obtained using Shining 3D scanner $^{1}$  and Artec Eva 3D scanner $^{2}$ , collected from OmniObject3D [41]. We normalize object models to fit within a  $(-1,1)^{3}(m^{3})$  three-dimensional space, and align objects within each category to a consistent canonical pose. In the latest dataset, Omni6D-xl builds upon and extends Omni6D, comprising 13,161 instances across an impressive span of 339 categories. For more details, please refer to Appendix Section C.",
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+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "**Rendering.** We employ stratified sampling to split instances within each category, subsequently dividing them into training, validation, and test sets in a 7:2:1 ratio. In the construction of our dataset, we utilize 9 room models from the Replica dataset as backdrops. For each scenery setup, we randomly select a room model to act as the background, along with 6 - 8 object instance models, which are allowed to perform free-fall motion within the room model, resulting in random scattering in a specific section of the room. Each object model is scaled by a random factor ranging from 0.8 to 1.2 as part of our data augmentation strategy. Considering the attention center of the combined instance models as the origin point, the camera randomly selects ten positions within a radius of  $8 - 9m$  and an elevation angle range between  $30 - 90^{\\circ}$ . The camera then performs rendering at these selected positions while facing towards the attention center.",
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+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "Setting. We utilize BlenderProc 2.5.0 [9] to implement the aforementioned rendering process. The intrinsic parameters of the camera are set to [577.5, 577.5, 319.5, 239.5], with an image size specified as  $640 \\times 480$ . Our approach ensures the diversity and breadth of the dataset, making it suitable for rigorous testing and yielding accurate results.",
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+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "3.2 Data Annotations",
+        "text_level": 1,
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "Rich Annotations. Each rendered output includes the ground truth class label, instance mask, NOCS mapping [40], depth map, as well as 6D pose and size. Fig. 1 exhibits a selection of rendered outputs. To reduce the storage size of the dataset, we encode high-precision depth maps into RGB images by multiplying depth by 10,000, rounding to nearest integer, and converting to base 256. The resulting three digits represent RGB channels.",
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "Rotational Invariance. Rotational invariance implies that a symmetric object can retain its original shape after rotation by certain angles. Many common objects have this property. As shown in Fig. 6, we define the coordinate system as a right-handed system with the x-axis pointing outwards and the y-axis oriented upwards. We contemplate three cases of rotational invariance where an object maintains its original shape after following rotations: any degrees (Sym-1), multiples of 90 degrees (Sym-2) and 180 degrees (Sym-3). Additionally, we denote the case of no rotational invariance around the axis as Sym-0. According to these definitions, all objects in Omni6D are annotated for their rotational symmetry around the xyz-axes. It's worth noting that symmetry attributes may differ among instances within the same category, requiring instance-level rather",
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+        "page_idx": 4
+    },
+    {
+        "type": "header",
+        "text": "Omni6D",
+        "bbox": [
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+        "page_idx": 4
+    },
+    {
+        "type": "page_number",
+        "text": "5",
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+    },
+    {
+        "type": "page_footnote",
+        "text": "<sup>1</sup> https://www.einscan.com/",
+        "bbox": [
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+    },
+    {
+        "type": "page_footnote",
+        "text": "$^{2}$  https://www.artec3d.cn/",
+        "bbox": [
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+        "page_idx": 4
+    },
+    {
+        "type": "image",
+        "img_path": "images/21a06c09d06bd4bceb684939b8b7eea0d18a22f25c84e14e1f783f15922c5b0f.jpg",
+        "image_caption": [
+            "Fig. 2: Symmetry statistics. The figure demonstrates different symmetry cases using object instances and provides a quantitative representation of the occurrence frequency for various combinations of distinct symmetry cases across the xyz-axes."
+        ],
+        "image_footnote": [],
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "than category-level annotations. Fig. 6 illustrates all kinds of symmetry cases using object instances and quantifies their occurrence frequency. Fig. 1 selects several examples to provide a more visual explanation of rotational invariance. These considerations are then integrated into our evaluation protocols in Sec. 4.2.",
+        "bbox": [
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+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "3.3 Dataset Statistics",
+        "text_level": 1,
+        "bbox": [
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+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "Spatial Statistics. Omni6D aims to overcome challenges in estimating poses for occluded object instances. Fig. 3a and Fig. 3b shows the spatial distribution of point clouds and objects by projecting their centroids on the XY-plane (top) and XZ-plane (bottom) [4]. Fig. 3c depicts the relative object size distribution, defined as the square root of the object-to-image area ratio. We observe that the spatial distribution of Omni6D is similar to that of CAMERA and REAL, with a greater resemblance to CAMERA despite having a closer depth range. However, a more pronounced discrepancy between the spatial distribution of point clouds and objects is evident in Omni6D compared to CAMERA and REAL. This observation suggests a higher occurrence of occlusion scenes in Omni6D, highlighting the intricate challenges it presents to 6D object pose estimation. Nonetheless, as depicted in Fig. 4a, algorithms trained on Omni6D demonstrate their robustness in tackling these complexities.",
+        "bbox": [
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+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "Angular Deviation. Omni6D enables accurate pose estimation using only the lower half or bottom appearance of objects. Fig. 3d depicts the density of angular deviations from the upward direction, i.e. y-axis. Our dataset displays a more uniform distribution of object angles relative to the upward axis and exhibits greater deviation from the canonical pose angles. Unlike NOCS, which primarily",
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+    {
+        "type": "page_number",
+        "text": "6",
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+    {
+        "type": "header",
+        "text": "M. Zhang et al.",
+        "bbox": [
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+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "image",
+        "img_path": "images/8fee4c399f65e261ce78ad239b6c70672ec07711d2cd00d69f74712914c2c832.jpg",
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+    {
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+    },
+    {
+        "type": "image",
+        "img_path": "images/7d37c58de0e61aa6e6d242ce52c589c3410f4c955e4e848f5f7bba27e754f4a4.jpg",
+        "image_caption": [
+            "(c) Relative 2D object size"
+        ],
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+        "page_idx": 6
+    },
+    {
+        "type": "image",
+        "img_path": "images/3d2a6dc09651eda420e55261b4d686340073f719d92e6cbff552b2ab33ce5d30.jpg",
+        "image_caption": [
+            "(d) Angular deviation"
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+    },
+    {
+        "type": "image",
+        "img_path": "images/c18e3c527805bd2f8dd6ab3bc6734b5033be4bee52a798a040a219a804cd5ad0.jpg",
+        "image_caption": [
+            "(e) Clustering results",
+            "Fig. 3: Omni6D analysis. (a) distribution of point cloud centroids, (b) distribution of object centroids on (top) normalized image, XY-plane, and (bottom) normalized depth, XZ-plane, (c) density of relative 2D object size, (d) density of angular deviation from the upward direction, (e) Omni6D dataset clustering results. The angle of each sector in the chart reflects the relative size of the instance count within that category."
+        ],
+        "image_footnote": [],
+        "bbox": [
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+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "uses upright object placement, Omni6D utilizes physical simulations for freefall object positioning [9]. As a result, it presents more challenging and diverse pose estimation scenes. Training on Omni6D enhances algorithms' robustness to object rotation angles, as evidenced by the image in Fig. 4b.",
+        "bbox": [
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+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "Shape Priors. We obtain the mean latent embedding and shape prior for each category from the variational autoencoder [5]. Fig. 1 showcases categorical shape priors, each displaying unique characteristics, facilitating an intuitive association between point cloud shapes and corresponding real-world entities. Meanwhile, Fig. 3e explains clustering results based on categorical latent embeddings, where we employ agglomerative clustering [27] to group categories into 20 clusters. It highlights the geometric coherence among semantically identical objects (especially man-made ones) in Omni6D dataset and further confirms that these categorical shape priors can effectively leverage the wealth of shape information from numerous similar objects to elucidate category features. These insights provide a theoretical basis for applications of category-level 6D object pose estimation using our Omni6D dataset.",
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+    {
+        "type": "text",
+        "text": "4 Evaluation and Analysis",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "4.1 Experimental Setup",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "Datasets. Our experimentation utilized two datasets, namely Omni6D and Omni6Dout. Omni6D are partitioned into training, validation, and test sets in a 7:2:1 ratio, denoted as Omni6Dtrain, Omni6Dval and Omni6Dtest respectively. These sets are further subdivided into subsets with increasing category sizes of 3, 6, 12, 24, and 48. We denote the subset containing  $n$  categories as c1sn. Each subset includes all classes present in the previous subset with additional",
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+    {
+        "type": "header",
+        "text": "Omni6D",
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+    {
+        "type": "page_number",
+        "text": "7",
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+    {
+        "type": "image",
+        "img_path": "images/71ab0bbfbe8f9e114c0dfa45c2482f0cda0dc4ae1eca47cd529806e901147a56.jpg",
+        "image_caption": [
+            "(a) Challenges from occluded object"
+        ],
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+        "page_idx": 7
+    },
+    {
+        "type": "image",
+        "img_path": "images/a884f1b76d3e22b47a9e9ae129f19ed947e2be829c487c81fbde988623259513.jpg",
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+    },
+    {
+        "type": "image",
+        "img_path": "images/8f6e3fcb3935ac8383fdd002bbc432fd4255be8def1274eebab5495446190d70.jpg",
+        "image_caption": [
+            "Fig. 4: Challenges from Omni6D. (a) Algorithms trained on Omni6D can overcome challenges in estimating poses for occluded object instances. The left shows an occluded object instance at the edge of the image, while the right image shows an object instance obstructed by other objects. (b) Algorithm trained on Omni6D can accurately estimate poses with only the lower half or bottom appearance of an object. The green and red colors respectively denote the ground truth and predicted 3D bounding boxes. The blue and orange lines on the boxes separately highlight the intersecting lines of the frontal face and the top face of the two 3D bounding boxes, while the darker lines indicate the bottom of the bounding boxes."
+        ],
+        "image_footnote": [],
+        "bbox": [
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+        "page_idx": 7
+    },
+    {
+        "type": "image",
+        "img_path": "images/586f974ef75a54088c7f57fd9a14b7105d4364da27f6377bab913ac916f10a06.jpg",
+        "image_caption": [
+            "(b) Challenges from bottom views"
+        ],
+        "image_footnote": [],
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "classes included to meet the desired total. Fig. 6a presents the specific categories included in c1sn and their respective sizes relative to each other. Omni6Dout is utilized as an additional test set to measure our algorithm's inter-category generalization. This dataset, constructed similarly to Omni6D, encompasses 52 models spanning 17 categories unseen in Omni6D, along with 4762 images. For additional details on datasets, please refer to the appendix.",
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+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "Details. All experiments are carried out on a server equipped with an Intel(R) Xeon(R) Gold 6248R CPU @ 3.00GHz and an NVIDIA A100-SXM4-80GB GPU. We maintain consistency in parameters and strategies throughout training, ensuring uniformity in our experiment environment. Given the challenges of semantic classification with a large vocabulary, we use ground truth masks to mitigate the impact of low-quality classification on pose estimation results.",
+        "bbox": [
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+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "4.2 Symmetry-Aware Evaluation",
+        "text_level": 1,
+        "bbox": [
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+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "Basic Evaluation Metrics. We utilize the average accuracy of Intersection over 3D Union (IoU) [14] in object detection, and  $n^{\\circ}m$  cm in pose estimation. We further decompose  $n^{\\circ}m$  cm [19,31] to individually evaluate the model's predictive error  $n^{\\circ}$  for pose and  $m$  cm for translation. For these three types of errors, the thresholds considered are  $\\{50\\%, 75\\%\\}$ ,  $\\{5^{\\circ}, 10^{\\circ}\\}$  and  $\\{2cm,5cm\\}$  [3, 30, 42]. Additionally, we set a detection threshold for objects requiring at least a  $10\\%$  overlap between predicted and ground-truth bounding boxes.",
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "Our Symmetry-Aware Metrics. Due to NOCS's limited categories, traditional algorithms mainly handle basic symmetry cases, such as rotational symmetry around the y-axis. However, Omni6D has a wider range of objects with different rotational invariances across multiple axes. Fig. 6 provides symmetry statistics for Omni6D objects. To alleviate this issue, we propose a symmetry-",
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+        "page_idx": 7
+    },
+    {
+        "type": "page_number",
+        "text": "8",
+        "bbox": [
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+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "header",
+        "text": "M. Zhang et al.",
+        "bbox": [
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+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "code",
+        "sub_type": "algorithm",
+        "code_caption": [
+            "Algorithm 1 Compute Our Symmetry-Aware Metric  $L_{s}$"
+        ],
+        "code_body": "1: procedure SYMMETRIC_METRIC(L, R,  $n_x$ ,  $n_y$ ,  $n_z$ )  \n2:  $\\Theta_0 = \\{0^\\circ\\}$   \n3:  $\\Theta_2 = \\{0^\\circ, 90^\\circ, 180^\\circ, 270^\\circ\\}$   \n4:  $\\Theta_3 = \\{0^\\circ, 180^\\circ\\}$  // Rotations around Sym-1 axis need not be considered.  \n5:  $c =$  count(1 occurrences in  $\\{n_x, n_y, n_z\\}$ )  \n6: if  $c \\geq 2$  then // The object is a sphere.  \n7:  $L_s = L(R^*, R)$   \n8: else if  $c == 1$  then // Rotations around Sym-1 axis can be disregarded.  \n9: Without loss of generality, assume  $n_x == 1$ .  \n10:  $L_s = \\min_{\\theta_y \\in \\Theta_{ny}, \\theta_z \\in \\Theta_{nz}} L(R_{\\theta_y, \\theta_z}^*, R)$   \n11: else if  $c == 0$  then // Simply enumerate all cases.  \n12:  $L_s = \\min_{\\theta_x \\in \\Theta_{nx}, \\theta_y \\in \\Theta_{ny}, \\theta_z \\in \\Theta_{nz}} L(R_{\\theta_x, \\theta_y, \\theta_z}^*, R)$   \n13: end if  \n14: return  $L_s$   \n15: end procedure",
+        "bbox": [
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+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "aware metric. Unlike prior works focusing solely on the y-axis, our method considers rotation symmetry around all three axes.",
+        "bbox": [
+            212,
+            357,
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+            386
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "We define the relevant variables as follows:  $L_{s}$  denotes our symmetry-aware metric,  $L$  denotes the original metric.  $R$  stands for the ground truth rotation matrix, while  $R^{*}$  represents the predicted rotation matrix.  $R_{\\theta_x,\\theta_y,\\theta_z}^*$  corresponds to the predicted rotation matrix after sequentially rotating by  $\\theta_{x},\\theta_{y}$ , and  $\\theta_{z}$  degrees around the xyz axes. The rotational invariance cases around the x, y, and z axes are denoted as Sym-  $n_{x}$ , Sym-  $n_{y}$ , and Sym-  $n_{z}$ , where  $n_x,n_y$ , and  $n_z$  are the respective rotation parameters. Objects that align with Sym-  $n$  around an axis maintain their original shape when rotated by an angle from  $\\Theta_{n}$ .",
+        "bbox": [
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+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "Since the Euler angles are compact [13], the most straightforward approach is to determine the category of rotational invariance for each axis  $\\{\\mathbf{x},\\mathbf{y},\\mathbf{z}\\}$  sequentially, as mentioned in 3.2. To simplify computations, we set  $\\Theta_0 = \\{0^\\circ \\}$ ,  $\\Theta_{1} = \\{0^{\\circ},1^{\\circ},\\dots,359^{\\circ}\\}$ ,  $\\Theta_{2} = \\{0^{\\circ},90^{\\circ},180^{\\circ},270^{\\circ}\\}$ ,  $\\Theta_{3} = \\{0^{\\circ},180^{\\circ}\\}$ . We can define  $L_{s}$  as  $L_{s} = \\min_{\\theta_{x}\\in \\Theta_{n_{x}},\\theta_{y}\\in \\Theta_{n_{y}},\\theta_{z}\\in \\Theta_{n_{z}}}L(R_{\\theta_{x},\\theta_{y},\\theta_{z}}^{*},R)$ .",
+        "bbox": [
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+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "However, due to the singularity of Euler angles [13], we can simplify the above rotation transformation. The pseudo-code implementation of our Symmetry-Aware Evaluation is provided in Algorithm 1. It allows us to simplify what was originally at most  $360^{3}$  computations to a maximum of only  $4^{3}$  computations.",
+        "bbox": [
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+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "4.3 Large-Vocabulary 6D Pose and Size Estimation",
+        "text_level": 1,
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "Performance on Omni6D. We present results of algorithms [6,10,34,46,47] trained on Omni6Dtrain and tested on Omni6Dtest. We compare their quantitative results in Tab. 2 and their qualitative results in Fig. S10 in Appendix. The performance disparity among algorithms for category-level 6D object pose estimation becomes markedly pronounced when applied to large-vocabulary datasets, in contrast to the more consistent performance previously observed on the Real and CAMERA datasets [40]. This highlights the inherent strengths and weaknesses across various model structures.",
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "This observation suggests the potential importance of our large-vocabulary dataset in uncovering the relative performance of different models. It appears",
+        "bbox": [
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+        "page_idx": 8
+    },
+    {
+        "type": "header",
+        "text": "Omni6D",
+        "bbox": [
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+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "page_number",
+        "text": "9",
+        "bbox": [
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+    },
+    {
+        "type": "table",
+        "img_path": "images/e6f45eb75e29e5ebdaca5cad65b230e7f225bfcab51f337ae31cac986691cce6.jpg",
+        "table_caption": [
+            "Table 2: Category-level performance on Omni6D dataset. Models are trained on Omni6Dtrain and tested on Omni6Dtest. Instances within each category in the test set are unseen during training, substantiating the algorithms' capacity to generalize within individual categories under large-vocabulary settings. Bold and underlined results indicate the best and second-best performers."
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Methods</td><td>Network</td><td>IoU50</td><td>IoU75</td><td>5°2cm</td><td>5°5cm</td><td>10°2cm</td><td>10°5cm</td><td>5°</td><td>10°</td><td>2cm</td><td>5cm</td></tr><tr><td>SPD [34]</td><td>implicit</td><td>44.56</td><td>20.37</td><td>7.55</td><td>9.56</td><td>14.76</td><td>19.23</td><td>10.68</td><td>21.02</td><td>37.49</td><td>70.09</td></tr><tr><td>SGPA [6]</td><td>implicit</td><td>36.34</td><td>14.44</td><td>4.78</td><td>6.84</td><td>10.13</td><td>15.03</td><td>8.49</td><td>17.73</td><td>25.57</td><td>59.18</td></tr><tr><td>DualPoseNet [20]</td><td>hybrid</td><td>58.84</td><td>25.49</td><td>8.28</td><td>9.30</td><td>17.26</td><td>19.05</td><td>9.38</td><td>19.18</td><td>73.82</td><td>96.37</td></tr><tr><td>RBP-Pose [46]</td><td>hybrid</td><td>35.92</td><td>4.66</td><td>0.37</td><td>0.60</td><td>0.53</td><td>0.80</td><td>0.75</td><td>0.96</td><td>39.73</td><td>83.55</td></tr><tr><td>GPV-Pose [10]</td><td>explicit</td><td>15.28</td><td>0.26</td><td>0.10</td><td>0.70</td><td>0.14</td><td>0.96</td><td>2.25</td><td>2.96</td><td>5.31</td><td>33.70</td></tr><tr><td>HS-Pose [47]</td><td>explicit</td><td>62.65</td><td>23.02</td><td>4.26</td><td>4.85</td><td>10.49</td><td>11.61</td><td>4.96</td><td>11.75</td><td>80.93</td><td>97.78</td></tr></table>",
+        "bbox": [
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+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "table",
+        "img_path": "images/585bedaa562e7b9201e8a889ed9f401c61069c0c42d843d52194ac43db91ff16.jpg",
+        "table_caption": [
+            "Table 3: Category-level performance on unseen categories. Models are trained on Omni6Dtrain and tested on Omni6Dout. Categories in the test set never appear in the training set, validating the algorithms' ability to generalize across categories."
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Methods</td><td>Network</td><td>IoU50</td><td>IoU75</td><td>5°2cm</td><td>5°5cm</td><td>10°2cm</td><td>10°5cm</td><td>5°</td><td>10°</td><td>2cm</td><td>5cm</td></tr><tr><td>SPD [34]</td><td>implicit</td><td>7.56</td><td>0.95</td><td>0.18</td><td>0.40</td><td>0.80</td><td>1.65</td><td>0.65</td><td>2.36</td><td>8.88</td><td>40.59</td></tr><tr><td>SGPA [6]</td><td>implicit</td><td>7.05</td><td>0.60</td><td>0.07</td><td>0.28</td><td>0.19</td><td>0.82</td><td>0.53</td><td>1.69</td><td>3.87</td><td>28.28</td></tr><tr><td>DualPoseNet [20]</td><td>hybrid</td><td>36.85</td><td>12.06</td><td>3.24</td><td>3.37</td><td>8.04</td><td>8.51</td><td>3.39</td><td>8.64</td><td>78.00</td><td>98.60</td></tr><tr><td>RBP-Pose [46]</td><td>hybrid</td><td>26.18</td><td>1.95</td><td>0.01</td><td>0.02</td><td>0.02</td><td>0.03</td><td>0.02</td><td>0.03</td><td>16.74</td><td>43.06</td></tr><tr><td>GPV-Pose [10]</td><td>explicit</td><td>10.97</td><td>0.14</td><td>0.03</td><td>0.18</td><td>0.12</td><td>0.57</td><td>0.30</td><td>1.07</td><td>7.14</td><td>41.30</td></tr><tr><td>HS-Pose [47]</td><td>explicit</td><td>36.75</td><td>8.92</td><td>1.54</td><td>1.66</td><td>4.67</td><td>5.16</td><td>1.75</td><td>5.38</td><td>79.95</td><td>98.27</td></tr></table>",
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "that the increased complexity of the dataset could push model architectures to their theoretical limits, possibly revealing intrinsic characteristics otherwise obscured in less complex scenarios. For example, SPD, SGPA is particularly proficient in predicting rotation, and SPD achieves the highest score in  $n^{\\circ}mcm$ . This could be due to its implicit network's propensity for generating more reliable rotational forecasts. Meanwhile, DualPoseNet and HS-Pose provide more accurate predictions for translation and score higher in IoU. This could be associated with the characteristic of models with explicit networks to produce better translations and size estimates.",
+        "bbox": [
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+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "Our large-vocabulary dataset, encompassing a broad spectrum of shapes and appearances, enables a comprehensive evaluation of diverse category-level pose estimation methods. This serves not only as a robust test of an algorithm's generalizability but also as a valuable tool in understanding the advantages offered by different algorithmic structures.",
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "Generalization Performance. We evaluate algorithms on  $\\mathrm{Omni6D}_{out}$  to assess their inter-category generalization capabilities. The outcomes are presented in Tab. 3. Notably, DualPoseNet and HS-Pose emerged as superior performers, outclassing others across all metrics, thereby demonstrating excellent generalization abilities. Contrastingly, implicit methods including SPD and SPGA exhibited marked limitations. Qualitative results are shown in Fig. S11 in Appendix.",
+        "bbox": [
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+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "Drawing parallels with the observations from Tab. 2, we found that metrics such as translation and IoU were relatively easier to excel in, suggesting superior",
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+    {
+        "type": "page_number",
+        "text": "10",
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+    {
+        "type": "header",
+        "text": "M. Zhang et al.",
+        "bbox": [
+            271,
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+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "image",
+        "img_path": "images/4570cd25a0b82c7cbb1192ae3e87459036b14737d54672c088e5deb7b36383b5.jpg",
+        "image_caption": [
+            "Fig. 5: Category-Wise Performance on Omni6D Dataset. The x-axis, moving from left to right, sequentially represents: the number of objects within a category (Semantic Category), the number of objects within a cluster clustered based on shape priors (Shape Category) and the diversity of instances within a category. The y-axis depicts category or clustered group results for IoU<sub>75</sub> and 5°2 cm metrics. Each plotted point illustrates the algorithm's result for a specific category or cluster, while the line showcases the trend of the linear fit for the scattered points."
+        ],
+        "image_footnote": [],
+        "bbox": [
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+        ],
+        "page_idx": 10
+    },
+    {
+        "type": "text",
+        "text": "generalization abilities in translation and size prediction. Conversely, the generalization of rotation emerges as a considerable challenge in category-level 6D object pose estimation, especially within large-vocabulary scenes.",
+        "bbox": [
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+        ],
+        "page_idx": 10
+    },
+    {
+        "type": "text",
+        "text": "Category-wise Analysis. Based on the  $\\mathrm{IoU}_{75}$  and  $5^{\\circ}2$  cm metrics, we conducted a detailed category-wise analysis of the results from Tab. 2. Left columns in Fig. 5 illustrate the correlation between category-level 6D pose estimation performance and the number of instances within each category in Omni6Dtrain. Middle columns in Fig. 5 analyze the correlation between cluster-level average performance and cluster size based on the clustering results described in Fig. 3e. We found that the performance of pose estimation for each category is more strongly correlated with the number of instances within clusters than with semantic categories, showing a positive correlation. This suggests that shape categories have a greater impact on training than semantic categories do. Notably, algorithms like SPD, SGPA, and RBP-Pose that utilize shape prior structures are particularly sensitive to this influence.",
+        "bbox": [
+            212,
+            478,
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+        ],
+        "page_idx": 10
+    },
+    {
+        "type": "text",
+        "text": "Right columns in Fig. 5 reveal the correlation of pose estimation performance relative to instance diversity within each category in the training set. We measured instance diversity by calculating the mean chamfer distance [1] among all pairs of instances in each category. The results show that as diversity within a category increases, pose estimation performance tends to improve. This observation aligns with the assertion made by [23]: The key to the success of prior-based methods lies in the deformation modules, which learns to synthesize world-space target objects and explicitly builds the correspondence between camera and world-space. As the number of instances increases and the diversity within a shape category expands, the model's capacity to learn deformation from priors to actual instance shapes is strengthened, leading to improved results.",
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+        ],
+        "page_idx": 10
+    },
+    {
+        "type": "header",
+        "text": "Omni6D",
+        "bbox": [
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+        ],
+        "page_idx": 10
+    },
+    {
+        "type": "page_number",
+        "text": "11",
+        "bbox": [
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+        ],
+        "page_idx": 10
+    },
+    {
+        "type": "image",
+        "img_path": "images/c85a544023dec18ffe03f680575a37364787a74cbda9b5523875a42459d5637a.jpg",
+        "image_caption": [
+            "(a) Category inventory of clsn"
+        ],
+        "image_footnote": [],
+        "bbox": [
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+            145,
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+        ],
+        "page_idx": 11
+    },
+    {
+        "type": "image",
+        "img_path": "images/e3ef99cf02c9d254bf358461b49c61e658fc66d4a7ae50b861cceaee9b6b7f9d.jpg",
+        "image_caption": [
+            "(b) Our finetune strategy",
+            "Fig.6: Our finetune strategy. (a) Category inventory of c1sn within Omni6D dataset. The angle of each sector in the chart reflects the relative size of the instance count within that category. (b) In each fine-tuning step, we double the category count, copying trained global features and old category parameters into the new network while initializing the new category parameters. An observable deepening of color is indicative of the escalating count of training iterations."
+        ],
+        "image_footnote": [],
+        "bbox": [
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+        ],
+        "page_idx": 11
+    },
+    {
+        "type": "text",
+        "text": "4.4 Fine-Tuning from Limited Categories",
+        "text_level": 1,
+        "bbox": [
+            214,
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+        ],
+        "page_idx": 11
+    },
+    {
+        "type": "text",
+        "text": "We propose a finetuning strategy that helps extend methods from a limited set of categories to large-vocabulary. We take SPD [34], DualPoseNet [20], and HS-Pose [47] as examples which belong to three different network architectures and show good performance on Omni6D $_{test}$ . We respectively take their best models on CAMERA as our pre-trained models.",
+        "bbox": [
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+        "page_idx": 11
+    },
+    {
+        "type": "text",
+        "text": "Initiating the fine-tuning process, we utilize three categories: bottle, bowl, and cup, which are concurrently present in both Omni6D and CAMERA datasets, aligning with the cls3 category. By facilitating the training on Omni6D-cls3, we enable a transfer of the model from CAMERA to Omni6D. Following the method illustrated in Fig. 6b, we engage in an iterative fine-tuning process on a progressively expanded category dataset until it reaches our desired number. In our experiments, we set this target number to be 48 categories.",
+        "bbox": [
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+        ],
+        "page_idx": 11
+    },
+    {
+        "type": "text",
+        "text": "In parallel, we conduct training from scratch separately on cls3, cls6, ..., and cls48 as a comparison, employing the same number of training iterations. As shown in Fig. 7, even with an exponential increase in the number of categories, pre-trained models remain pivotal in our fine-tuning strategy. The performance of fine-tuning consistently outperforms that of training from scratch.",
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "However, regardless of whether the training approach is finetuning or training from scratch, a decline in performance is observed as the number of categories increases. The decline rates for SPD and DualPoseNet are slower, coupled with an initial augmentation in performance due to increased training data and iterations. In contrast, HS-Pose experiences a more rapid decline, with fine-tuned  $5^{\\circ}2$  cm results dropping from initial  $62.52\\%$  to  $14.42\\%$ . Models that excel in tasks involving a limited number of categories may not necessarily maintain their superiority in large-vocabulary tasks, they might be surpassed by models that are more robust and easier to train.",
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+    {
+        "type": "page_number",
+        "text": "12",
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+    {
+        "type": "header",
+        "text": "M. Zhang et al.",
+        "bbox": [
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+        ],
+        "page_idx": 11
+    },
+    {
+        "type": "image",
+        "img_path": "images/ba9ac324d8671bccc8c28c4b9fcfa9dcde22967901e28bfec40073491d65830a.jpg",
+        "image_caption": [
+            "Fig. 7: Finetuned results. Each figure's x-axis represents the number of categories in the training and test set, while the y-axis displays the outcomes of  $5^{\\circ}2$  cm,  $5^{\\circ}$  and  $2cm$  metrics. Each row, from top to bottom, sequentially employs three methods: SPD [34], DualPoseNet [20], and HS-Pose [47]. The figures depict the outcomes derived from two training strategies as the number of training categories increases, accompanied by the gradual expansion of corresponding test sets."
+        ],
+        "image_footnote": [],
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+    {
+        "type": "text",
+        "text": "4.5 Visual Realism",
+        "text_level": 1,
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+        "type": "text",
+        "text": "Due to the complexity of collecting and annotating real-world data, contemporary datasets like NOCS [40] are composed of a large amount of synthetic data and a small portion of real-world data. While collecting real data is relatively straightforward when the number of categories is limited, gathering well-annotated real-world data for pose estimation tasks involving large vocabulary categories becomes a monumental task.",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "Our Omni6D dataset, which includes large vocabulary objects, is also derived from rendering. However, the incorporation of real-scanned objects significantly enhances the realism of the rendered images. As depicted in Fig. 8, Omni6D receives a score of  $2.69 \\pm 0.39$ , surpassing the results obtained by CAMERA.",
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "Given these significant advantages, our dataset excels not only in large-vocabulary scenarios but also in real-world scenes. As depicted in Tab. 4, We use DualPoseNet [20] to train on the common categories in REAL [40] and Omni6D, namely bottles, bowls, and mugs. We train separately on the two datasets and their mix. The results show that Omni6D models perform well on REAL275, and training on the mixed dataset outperforms using REAL or Omni6D datasets alone. This demonstrates that our dataset enables the direct transfer of models to real-world scenes. Moreover, it seamlessly supplements the existing real-world dataset, enabling joint training of models on our dataset and the real-world data.",
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "To further validate the sim2real capability of models trained with Omni6D, we constructed a real-world dataset, Omni6D-Real, comprising 30 scenes, 39 categories, 73 instances, and 1k images. We captured RGBD images with Azure",
+        "bbox": [
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+    },
+    {
+        "type": "header",
+        "text": "Omni6D",
+        "bbox": [
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+        "page_idx": 12
+    },
+    {
+        "type": "page_number",
+        "text": "13",
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+    {
+        "type": "image",
+        "img_path": "images/38cafe713d1ef714db0a8789a5bbedf739e0033b567d6dbaa80f835060781305.jpg",
+        "image_caption": [
+            "Omni6D  $2.69\\pm 0.39$"
+        ],
+        "image_footnote": [],
+        "bbox": [
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+    },
+    {
+        "type": "image",
+        "img_path": "images/55df81ccc0ce4ee128d7ec79f7423764222c93e70de8753013ec6fbeee0fe8d8.jpg",
+        "image_caption": [
+            "CAMERA  $1.55\\pm 0.08$",
+            "Fig. 8: Comparison of Visual Realism. We evaluated the visual realism of Omni6D in comparison to other datasets through a survey involving 70 human subjects. We randomly selected 10 images from each dataset and introduced noise by blending in 5 images from COCO [21], which included captured photos, and  $\\mathrm{SKETCH}^4$ , which comprised rendered images. Subjects were asked to rate the realism of sampled images on a scale from 1 (least realistic) to 5 (most realistic). We report the mean and standard deviation and include a sampled image from the study."
+        ],
+        "image_footnote": [],
+        "bbox": [
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+    },
+    {
+        "type": "image",
+        "img_path": "images/7b9cffb67d1dbd3750ea2af15078b90bead68e931b3c33ee7a8e542584b0f8ab.jpg",
+        "image_caption": [
+            "REAL  $3.53\\pm 0.28$"
+        ],
+        "image_footnote": [],
+        "bbox": [
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+    {
+        "type": "image",
+        "img_path": "images/f37437a1c1f431216714574d7ee1872c7966b598f3e50fea098885e97b7140f2.jpg",
+        "image_caption": [
+            "WILD6D  $4.38\\pm 0.23$"
+        ],
+        "image_footnote": [],
+        "bbox": [
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+    {
+        "type": "table",
+        "img_path": "images/c8adf5872a9f5b977cc30b55b749aaba40ae431fc9353a762329178fda12eee1.jpg",
+        "table_caption": [
+            "Table 4: Performance on REAL275 with Different Training Sets. It compares how different training sets influence DualPoseNet's performance on REAL275 [40], providing insights into the model's ability to generalize in real-world tasks using Omni6D."
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Train data</td><td>Realism</td><td>IoU50</td><td>IoU75</td><td>5°2cm</td><td>5°5cm</td><td>10°2cm</td><td>10°5cm</td><td>5°</td><td>2cm</td></tr><tr><td>Omni6D</td><td>Real-Scanned</td><td>78.76</td><td>32.69</td><td>6.55</td><td>8.80</td><td>15.00</td><td>21.38</td><td>11.20</td><td>49.54</td></tr><tr><td>REAL [40]</td><td>Real</td><td>84.51</td><td>43.43</td><td>8.76</td><td>10.40</td><td>21.24</td><td>25.39</td><td>13.01</td><td>69.46</td></tr><tr><td>REAL+Omni6D</td><td>Mixed</td><td>85.28</td><td>58.59</td><td>14.10</td><td>17.83</td><td>30.10</td><td>38.97</td><td>20.96</td><td>71.00</td></tr></table>",
+        "bbox": [
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+        "page_idx": 13
+    },
+    {
+        "type": "text",
+        "text": "Kinect  $\\mathrm{DK}^3$  and preprocessed them using SAM [18] for object masks and ICP [2] for point cloud registration. Details are provided in Appendix Section D.",
+        "bbox": [
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+        "page_idx": 13
+    },
+    {
+        "type": "text",
+        "text": "5 Conclusion",
+        "text_level": 1,
+        "bbox": [
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+        "page_idx": 13
+    },
+    {
+        "type": "text",
+        "text": "In conclusion, this paper introduces Omni6D, a novel 6D pose estimation dataset with large-vocabulary categories and intricate scenes. We evaluate existing category-level 6D object pose estimation methods on this benchmark, analyze its challenges, and propose a fine-tuning strategy for large-vocabulary scenarios. Limitations. Our dataset, though more complex, doesn't fully encompass all real-world challenges. Additionally, our fine-tuning strategy effectively extends methods from a small set to a larger one, but its efficacy may decrease with growing category diversity.",
+        "bbox": [
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+        "page_idx": 13
+    },
+    {
+        "type": "text",
+        "text": "Future Work. Our study paves the way for diverse research avenues. An immediate next step is expanding the Omni6D dataset with more object types and scenes for comprehensive coverage. Additionally, annotating videos for scanned objects will validate algorithms' large-vocab pose estimation in real-world scenarios. Designing new training strategies for coping with increasing category diversity presents an intriguing challenge.",
+        "bbox": [
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+        "page_idx": 13
+    },
+    {
+        "type": "page_number",
+        "text": "14",
+        "bbox": [
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+        ],
+        "page_idx": 13
+    },
+    {
+        "type": "header",
+        "text": "M. Zhang et al.",
+        "bbox": [
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+        ],
+        "page_idx": 13
+    },
+    {
+        "type": "page_footnote",
+        "text": "<sup>3</sup> https://learn.microsoft.com/azure/kinect-dk/",
+        "bbox": [
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+        "page_idx": 13
+    },
+    {
+        "type": "page_footnote",
+        "text": "4 https://sketchfab.com/",
+        "bbox": [
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+        ],
+        "page_idx": 13
+    },
+    {
+        "type": "text",
+        "text": "Acknowledgements",
+        "text_level": 1,
+        "bbox": [
+            217,
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+        ],
+        "page_idx": 14
+    },
+    {
+        "type": "text",
+        "text": "This research is supported by Shanghai Artificial Intelligence Laboratory, the Centre for Perceptual and Interactive Intelligence (CPII) Ltd under the Innovation and Technology Commission (ITC)'s InnoHK, the Ministry of Education, Singapore, under its MOE AcRF Tier 2 (MOET2EP20221-0012), NTU NAP, and under the RIE2020 Industry Alignment Fund - Industry Collaboration Projects (IAF-ICP) Funding Initiative.",
+        "bbox": [
+            212,
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+        "page_idx": 14
+    },
+    {
+        "type": "text",
+        "text": "References",
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+        ],
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2024/Omni6D_ Large-Vocabulary 3D Object Dataset for Category-Level 6D Object Pose Estimation/full.md

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+# Omni6D: Large-Vocabulary 3D Object Dataset for Category-Level 6D Object Pose Estimation
+
+Mengchen Zhang $^{1,2}$ , Tong Wu $^{3}$ , Tai Wang $^{2}$ , Tengfei Wang $^{2}$ , Ziwei Liu $^{4}$ , and Dahua Lin $^{2,3}$
+
+<sup>1</sup> Zhejiang University, Zhejiang, China
+
+$^{2}$  Shanghai Artificial Intelligence Laboratory, Shanghai, China
+
+3 The Chinese University of Hong Kong, Hong Kong SAR
+
+$^{4}$  Nanyang Technological University, Singapore
+
+{zhangmengchen, wangtai, wangtengfei}@pjlab.org.cn,
+
+{wt020,dhlin}@ie.cuhk.edu.hk,ziwei.liu@ntu.edu.sg
+
+![](images/55b5a6610150eb955d988781bccb266b91dce88136f8d16961fa3019f16d46c2.jpg)  
+Fig.1: Omni6D is a dataset for 6D object pose and size estimation with large vocabulary categories and rich annotations. (a) showcases ground truth of RGB image, depth map and NOCS map. (b) presents shape priors derived from a variational autoencoder [5] with adjusted canonical poses. (c) provides examples of the rotational symmetry of objects we have annotated, indicating the multiples of angles by which the shape remains unchanged when rotated around the xyz axes.
+
+Abstract. 6D object pose estimation aims at determining an object's translation, rotation, and scale, typically from a single RGBD image. Recent advancements have expanded this estimation from instance-level to category-level, allowing models to generalize across unseen instances within the same category. However, this generalization is limited by the narrow range of categories covered by existing datasets, such as NOCS, which also tend to overlook common real-world challenges like occlusion. To tackle these challenges, we introduce Omni6D, a comprehensive RGBD dataset featuring a wide range of categories and varied backgrounds, elevating the task to a more realistic context. 1) The dataset comprises an extensive spectrum of 166 categories, 4688 instances adjusted to the canonical pose, and over 0.8 million captures, significantly broadening the scope for evaluation. 2) We introduce a symmetry-aware
+
+metric and conduct systematic benchmarks of existing algorithms on Omni6D, offering a thorough exploration of new challenges and insights. 3) Additionally, we propose an effective fine-tuning approach that adapts models from previous datasets to our extensive vocabulary setting. We believe this initiative will pave the way for new insights and substantial progress in both the industrial and academic fields, pushing forward the boundaries of general 6D pose estimation.
+
+Keywords: 6DoF Pose Estimation  $\cdot$  Large Vocabulary Dataset  $\cdot$  Metrics and Benchmarks
+
+# 1 Introduction
+
+6D pose estimation aims at predicting the position, orientation, and size of objects in a 3D space using RGB (D) images, enabling various applications such as augmented/virtual reality [26,33], robot manipulation [11,35], and scene understanding [15,28].
+
+Early instance-level pose estimation approaches [32, 38, 39, 42, 43] typically involve providing instance CAD models and predicting poses of instances that were seen during training, restricting the generalization to unseen objects. In contrast, recent research has shifted towards category-level 6D object pose estimation [6-8, 10, 16, 17, 20, 24, 25, 29, 34, 37, 40, 44-47], which learns category prior from a large number of instances within a category, allowing for pose estimation of new instances within the same category without the need for CAD models. By learning on a diverse range of categories, category-level approaches could be a more versatile solution for 6D pose estimation in real-world scenarios.
+
+However, most existing datasets [22, 40, 44] are limited to a small number of object categories, typically less than 10, as shown in Tab. 1, hindering their practical applicability to complex scenes.
+
+To overcome the limitations in previous category-level 6D pose estimation datasets, such as limited category numbers, lack of instance diversity within categories, and overly simplistic scenes, this paper presents a novel category-level dataset dubbed Omni6D for 6D pose estimation. Omni6D significantly extends the number of object categories to 166, and includes 4,688 real-scanned and well-annotated instance objects with a diverse range of shapes, sizes, and textures. The constructed benchmark includes 0.8M images featuring complex scenes with various occlusions, changing lighting conditions, complex backgrounds, and varying viewpoints. For each scene, we provide the rendered image, depth map, NOCS map, and instance mask. Also, considering the widespread rotational symmetry in objects, we examine three types of rotational invariance where an object maintains its original shape under following rotations: any degrees (Sym-1), multiples of 90 degrees (Sym-2) and 180 degrees (Sym-3). Additionally, we introduce a symmetry-aware metric to specifically address rotational invariance. Every object in Omni6D is adjusted to the canonical pose and annotated with rotational symmetry around three axes.
+
+Table 1: Comparisons between Omni6D and existing datasets. Omni6D significantly extends the range of everyday object categories and instances.  
+
+<table><tr><td>Datasets</td><td>Mode</td><td>Realism</td><td># Categories # Instances</td><td># Images</td></tr><tr><td>ShapeNet-SRNCars [22]</td><td>RGB</td><td>Synthetic</td><td>1</td><td>3514</td></tr><tr><td>Sim2Real Cars [22]</td><td>RGB</td><td>Real</td><td>1</td><td>10</td></tr><tr><td>CAMERA [40]</td><td>RGBD</td><td>Synthetic</td><td>6</td><td>1085</td></tr><tr><td>REAL [40]</td><td>RGBD</td><td>Real</td><td>6</td><td>42</td></tr><tr><td>Wild6D [44]</td><td>RGBD</td><td>Real</td><td>5</td><td>1722</td></tr><tr><td>Omni6D</td><td>RGBD</td><td>Real-Scanned</td><td>166</td><td>4,688</td></tr></table>
+
+Including a broader range of categories, our dataset offers a more comprehensive and challenging evaluation benchmark for category-level 6D object pose estimation. Utilizing Omni6D, we train and analyze existing algorithms, initiating a profound exploration of the challenges and vital elements involved in category-level estimation within large-vocabulary categories. Additionally, we assess these algorithms' capability to generalize across categories, and carry out a category-wise analysis. Experiments show that our dataset presents a more challenging benchmark for 6D pose estimation, highlighting the need for more robust and generalized pose estimation approaches. As an initial attempt, we present a finetuning strategy that assists in broadening the scope of existing approaches from a limited range of categories to a broader vocabulary. Moreover, we conduct an analysis of the domain gap between our dataset and real-world dataset, emphasizing the benefits of their combined use.
+
+Our dataset will be publicly available to the research community, which will foster future research on more practical and robust 6D pose estimation algorithms and pave the way for broader applications.
+
+# 2 Related Work
+
+Existing work on category-level 6D object pose estimation can be generally divided into two types. After extracting features from images or point clouds, they compute Rotation, Translation, and Size (RTS) either through implicit point correspondence or explicit regression.
+
+Existing Datasets. The most commonly used dataset for category-level 6D object pose estimation is NOCS [40], comprising both the synthetic CAMERA dataset and the real-world REAL dataset. CAMERA includes 300k RGBD images of 31 indoor scenes with 1,085 object instances across 6 categories, while REAL mirrors the categories in CAMERA and includes 8k RGBD images capturing 42 instances in 18 real scenes. Wild6D [44] consists of 5,166 videos with 1.1 million images over 1,722 object instances in 5 categories. ShapeNet-SRN Cars dataset and Sim2Real Cars dataset proposed in iNerf [22] both exclusively include a single car category. The former includes 3,514 instances derived from ShapeNet cars, while the latter is extracted from videos capturing 10 distinct unseen car models. These datasets are limited by their narrow range of categories, hindering their ability to generalize broadly. Additionally, most training images
+
+are synthetic and lack realism, and their scenes are overly simplified, failing to account for common real-world challenges like occlusions.
+
+Implicit Methods. Implicit methods are based on point correspondence [6, 20, 24, 34, 37, 40, 44, 46]. NOCS [40], one of the pioneering works in this area, introduced the concept of Normalized Object Coordinate Space (NOCS). The final pose and size of the object are obtained by matching the predicted NOCS map with the observed depth input using the Umeyama algorithm [36] and RANSAC algorithm [12].
+
+Subsequent algorithms such as DualPoseNet, RBP-Net and RePoNet [20,44, 46] have continued to develop along the vein of NOCS, implicitly solving for pose after predicting the NOCS map. SPD [34] proposed a category-level shape prior, subsequently deforming this shape prior (i.e., average shape) to fit observed point cloud. SGPA, RePoNet, and CATRE [6,24,44] continue to develop along SPD's category-level shape prior approach. Algorithms like 6-PACK and SGPA [6,37] extract low-rank structure points, i.e., keypoints, from dense observed point clouds. 6-PACK [37] predicts interframe motion of target instances through keypoint matching, while SGPA [6] employs keypoints for more effective incorporation of sparse structural information during prior adaptation. These methods rely heavily on the RANSAC process to eliminate outliers, making them non-differentiable and time-consuming.
+
+Explicit Methods. Explicit methods are based on direct pose regression [7, 10, 20, 24, 46, 47]. DualPoseNet and RBP-Net [20, 46] conduct both explicit and implicit training, where one parallel pose decoder explicitly regresses the pose. CATRE [24], recognizes the inherent difference between estimations of rotation and translation/size, explicitly regressing their residuals and carrying out an iterative pose estimation process. FS-Net [7] designs an autoencoder with 3D Graphic Convolution for latent feature extraction and separates the predictions for rotation and translation/size into two distinct networks: one estimates translation/size through two residuals, while the other handles rotation prediction by estimating deflections on two orthogonal axes. GPV-Pose and HS-Pose [10, 47] utilize the same foundational mechanism introduced by FS-Net [7]. GPV-Pose [10] proposes a decoupled confidence-driven rotation representation that facilitates geometrically-aware recovery of correlated rotation matrices and introduces a new geometry-guided point-by-point voting paradigm for robust retrieval of 3D object bounding boxes. Meanwhile, HS-Pose [47] extends 3D-GC to extract mixed-range latent features from point cloud data through a simple network structure known as the HS layer.
+
+# 3 Omni6D Dataset
+
+# 3.1 Construction
+
+Dataset Collection. As shown in Tab. 1, Omni6D comprises 4,688 instances across an impressive span of 166 categories. Each instance is a high-resolution
+
+textured mesh, obtained using Shining 3D scanner $^{1}$  and Artec Eva 3D scanner $^{2}$ , collected from OmniObject3D [41]. We normalize object models to fit within a  $(-1,1)^{3}(m^{3})$  three-dimensional space, and align objects within each category to a consistent canonical pose. In the latest dataset, Omni6D-xl builds upon and extends Omni6D, comprising 13,161 instances across an impressive span of 339 categories. For more details, please refer to Appendix Section C.
+
+**Rendering.** We employ stratified sampling to split instances within each category, subsequently dividing them into training, validation, and test sets in a 7:2:1 ratio. In the construction of our dataset, we utilize 9 room models from the Replica dataset as backdrops. For each scenery setup, we randomly select a room model to act as the background, along with 6 - 8 object instance models, which are allowed to perform free-fall motion within the room model, resulting in random scattering in a specific section of the room. Each object model is scaled by a random factor ranging from 0.8 to 1.2 as part of our data augmentation strategy. Considering the attention center of the combined instance models as the origin point, the camera randomly selects ten positions within a radius of  $8 - 9m$  and an elevation angle range between  $30 - 90^{\circ}$ . The camera then performs rendering at these selected positions while facing towards the attention center.
+
+Setting. We utilize BlenderProc 2.5.0 [9] to implement the aforementioned rendering process. The intrinsic parameters of the camera are set to [577.5, 577.5, 319.5, 239.5], with an image size specified as  $640 \times 480$ . Our approach ensures the diversity and breadth of the dataset, making it suitable for rigorous testing and yielding accurate results.
+
+# 3.2 Data Annotations
+
+Rich Annotations. Each rendered output includes the ground truth class label, instance mask, NOCS mapping [40], depth map, as well as 6D pose and size. Fig. 1 exhibits a selection of rendered outputs. To reduce the storage size of the dataset, we encode high-precision depth maps into RGB images by multiplying depth by 10,000, rounding to nearest integer, and converting to base 256. The resulting three digits represent RGB channels.
+
+Rotational Invariance. Rotational invariance implies that a symmetric object can retain its original shape after rotation by certain angles. Many common objects have this property. As shown in Fig. 6, we define the coordinate system as a right-handed system with the x-axis pointing outwards and the y-axis oriented upwards. We contemplate three cases of rotational invariance where an object maintains its original shape after following rotations: any degrees (Sym-1), multiples of 90 degrees (Sym-2) and 180 degrees (Sym-3). Additionally, we denote the case of no rotational invariance around the axis as Sym-0. According to these definitions, all objects in Omni6D are annotated for their rotational symmetry around the xyz-axes. It's worth noting that symmetry attributes may differ among instances within the same category, requiring instance-level rather
+
+![](images/21a06c09d06bd4bceb684939b8b7eea0d18a22f25c84e14e1f783f15922c5b0f.jpg)  
+Fig. 2: Symmetry statistics. The figure demonstrates different symmetry cases using object instances and provides a quantitative representation of the occurrence frequency for various combinations of distinct symmetry cases across the xyz-axes.
+
+than category-level annotations. Fig. 6 illustrates all kinds of symmetry cases using object instances and quantifies their occurrence frequency. Fig. 1 selects several examples to provide a more visual explanation of rotational invariance. These considerations are then integrated into our evaluation protocols in Sec. 4.2.
+
+# 3.3 Dataset Statistics
+
+Spatial Statistics. Omni6D aims to overcome challenges in estimating poses for occluded object instances. Fig. 3a and Fig. 3b shows the spatial distribution of point clouds and objects by projecting their centroids on the XY-plane (top) and XZ-plane (bottom) [4]. Fig. 3c depicts the relative object size distribution, defined as the square root of the object-to-image area ratio. We observe that the spatial distribution of Omni6D is similar to that of CAMERA and REAL, with a greater resemblance to CAMERA despite having a closer depth range. However, a more pronounced discrepancy between the spatial distribution of point clouds and objects is evident in Omni6D compared to CAMERA and REAL. This observation suggests a higher occurrence of occlusion scenes in Omni6D, highlighting the intricate challenges it presents to 6D object pose estimation. Nonetheless, as depicted in Fig. 4a, algorithms trained on Omni6D demonstrate their robustness in tackling these complexities.
+
+Angular Deviation. Omni6D enables accurate pose estimation using only the lower half or bottom appearance of objects. Fig. 3d depicts the density of angular deviations from the upward direction, i.e. y-axis. Our dataset displays a more uniform distribution of object angles relative to the upward axis and exhibits greater deviation from the canonical pose angles. Unlike NOCS, which primarily
+
+![](images/8fee4c399f65e261ce78ad239b6c70672ec07711d2cd00d69f74712914c2c832.jpg)
+
+![](images/39c29b7165ee116496b5b46d2b01809f3ea22361cecf798569bb9851a1b529a0.jpg)
+
+![](images/7d37c58de0e61aa6e6d242ce52c589c3410f4c955e4e848f5f7bba27e754f4a4.jpg)  
+(c) Relative 2D object size
+
+![](images/3d2a6dc09651eda420e55261b4d686340073f719d92e6cbff552b2ab33ce5d30.jpg)  
+(d) Angular deviation
+
+![](images/c18e3c527805bd2f8dd6ab3bc6734b5033be4bee52a798a040a219a804cd5ad0.jpg)  
+(e) Clustering results  
+Fig. 3: Omni6D analysis. (a) distribution of point cloud centroids, (b) distribution of object centroids on (top) normalized image, XY-plane, and (bottom) normalized depth, XZ-plane, (c) density of relative 2D object size, (d) density of angular deviation from the upward direction, (e) Omni6D dataset clustering results. The angle of each sector in the chart reflects the relative size of the instance count within that category.
+
+uses upright object placement, Omni6D utilizes physical simulations for freefall object positioning [9]. As a result, it presents more challenging and diverse pose estimation scenes. Training on Omni6D enhances algorithms' robustness to object rotation angles, as evidenced by the image in Fig. 4b.
+
+Shape Priors. We obtain the mean latent embedding and shape prior for each category from the variational autoencoder [5]. Fig. 1 showcases categorical shape priors, each displaying unique characteristics, facilitating an intuitive association between point cloud shapes and corresponding real-world entities. Meanwhile, Fig. 3e explains clustering results based on categorical latent embeddings, where we employ agglomerative clustering [27] to group categories into 20 clusters. It highlights the geometric coherence among semantically identical objects (especially man-made ones) in Omni6D dataset and further confirms that these categorical shape priors can effectively leverage the wealth of shape information from numerous similar objects to elucidate category features. These insights provide a theoretical basis for applications of category-level 6D object pose estimation using our Omni6D dataset.
+
+# 4 Evaluation and Analysis
+
+# 4.1 Experimental Setup
+
+Datasets. Our experimentation utilized two datasets, namely Omni6D and Omni6Dout. Omni6D are partitioned into training, validation, and test sets in a 7:2:1 ratio, denoted as Omni6Dtrain, Omni6Dval and Omni6Dtest respectively. These sets are further subdivided into subsets with increasing category sizes of 3, 6, 12, 24, and 48. We denote the subset containing  $n$  categories as c1sn. Each subset includes all classes present in the previous subset with additional
+
+![](images/71ab0bbfbe8f9e114c0dfa45c2482f0cda0dc4ae1eca47cd529806e901147a56.jpg)  
+(a) Challenges from occluded object
+
+![](images/a884f1b76d3e22b47a9e9ae129f19ed947e2be829c487c81fbde988623259513.jpg)
+
+![](images/8f6e3fcb3935ac8383fdd002bbc432fd4255be8def1274eebab5495446190d70.jpg)  
+Fig. 4: Challenges from Omni6D. (a) Algorithms trained on Omni6D can overcome challenges in estimating poses for occluded object instances. The left shows an occluded object instance at the edge of the image, while the right image shows an object instance obstructed by other objects. (b) Algorithm trained on Omni6D can accurately estimate poses with only the lower half or bottom appearance of an object. The green and red colors respectively denote the ground truth and predicted 3D bounding boxes. The blue and orange lines on the boxes separately highlight the intersecting lines of the frontal face and the top face of the two 3D bounding boxes, while the darker lines indicate the bottom of the bounding boxes.
+
+![](images/586f974ef75a54088c7f57fd9a14b7105d4364da27f6377bab913ac916f10a06.jpg)  
+(b) Challenges from bottom views
+
+classes included to meet the desired total. Fig. 6a presents the specific categories included in c1sn and their respective sizes relative to each other. Omni6Dout is utilized as an additional test set to measure our algorithm's inter-category generalization. This dataset, constructed similarly to Omni6D, encompasses 52 models spanning 17 categories unseen in Omni6D, along with 4762 images. For additional details on datasets, please refer to the appendix.
+
+Details. All experiments are carried out on a server equipped with an Intel(R) Xeon(R) Gold 6248R CPU @ 3.00GHz and an NVIDIA A100-SXM4-80GB GPU. We maintain consistency in parameters and strategies throughout training, ensuring uniformity in our experiment environment. Given the challenges of semantic classification with a large vocabulary, we use ground truth masks to mitigate the impact of low-quality classification on pose estimation results.
+
+# 4.2 Symmetry-Aware Evaluation
+
+Basic Evaluation Metrics. We utilize the average accuracy of Intersection over 3D Union (IoU) [14] in object detection, and  $n^{\circ}m$  cm in pose estimation. We further decompose  $n^{\circ}m$  cm [19,31] to individually evaluate the model's predictive error  $n^{\circ}$  for pose and  $m$  cm for translation. For these three types of errors, the thresholds considered are  $\{50\%, 75\%\}$ ,  $\{5^{\circ}, 10^{\circ}\}$  and  $\{2cm,5cm\}$  [3, 30, 42]. Additionally, we set a detection threshold for objects requiring at least a  $10\%$  overlap between predicted and ground-truth bounding boxes.
+
+Our Symmetry-Aware Metrics. Due to NOCS's limited categories, traditional algorithms mainly handle basic symmetry cases, such as rotational symmetry around the y-axis. However, Omni6D has a wider range of objects with different rotational invariances across multiple axes. Fig. 6 provides symmetry statistics for Omni6D objects. To alleviate this issue, we propose a symmetry-
+
+Algorithm 1 Compute Our Symmetry-Aware Metric  $L_{s}$  
+1: procedure SYMMETRIC_METRIC(L, R,  $n_x$ ,  $n_y$ ,  $n_z$ )  
+2:  $\Theta_0 = \{0^\circ\}$   
+3:  $\Theta_2 = \{0^\circ, 90^\circ, 180^\circ, 270^\circ\}$   
+4:  $\Theta_3 = \{0^\circ, 180^\circ\}$  // Rotations around Sym-1 axis need not be considered.  
+5:  $c =$  count(1 occurrences in  $\{n_x, n_y, n_z\}$ )  
+6: if  $c \geq 2$  then // The object is a sphere.  
+7:  $L_s = L(R^*, R)$   
+8: else if  $c == 1$  then // Rotations around Sym-1 axis can be disregarded.  
+9: Without loss of generality, assume  $n_x == 1$ .  
+10:  $L_s = \min_{\theta_y \in \Theta_{ny}, \theta_z \in \Theta_{nz}} L(R_{\theta_y, \theta_z}^*, R)$   
+11: else if  $c == 0$  then // Simply enumerate all cases.  
+12:  $L_s = \min_{\theta_x \in \Theta_{nx}, \theta_y \in \Theta_{ny}, \theta_z \in \Theta_{nz}} L(R_{\theta_x, \theta_y, \theta_z}^*, R)$   
+13: end if  
+14: return  $L_s$   
+15: end procedure
+
+aware metric. Unlike prior works focusing solely on the y-axis, our method considers rotation symmetry around all three axes.
+
+We define the relevant variables as follows:  $L_{s}$  denotes our symmetry-aware metric,  $L$  denotes the original metric.  $R$  stands for the ground truth rotation matrix, while  $R^{*}$  represents the predicted rotation matrix.  $R_{\theta_x,\theta_y,\theta_z}^*$  corresponds to the predicted rotation matrix after sequentially rotating by  $\theta_{x},\theta_{y}$ , and  $\theta_{z}$  degrees around the xyz axes. The rotational invariance cases around the x, y, and z axes are denoted as Sym-  $n_{x}$ , Sym-  $n_{y}$ , and Sym-  $n_{z}$ , where  $n_x,n_y$ , and  $n_z$  are the respective rotation parameters. Objects that align with Sym-  $n$  around an axis maintain their original shape when rotated by an angle from  $\Theta_{n}$ .
+
+Since the Euler angles are compact [13], the most straightforward approach is to determine the category of rotational invariance for each axis  $\{\mathbf{x},\mathbf{y},\mathbf{z}\}$  sequentially, as mentioned in 3.2. To simplify computations, we set  $\Theta_0 = \{0^\circ \}$ ,  $\Theta_{1} = \{0^{\circ},1^{\circ},\dots,359^{\circ}\}$ ,  $\Theta_{2} = \{0^{\circ},90^{\circ},180^{\circ},270^{\circ}\}$ ,  $\Theta_{3} = \{0^{\circ},180^{\circ}\}$ . We can define  $L_{s}$  as  $L_{s} = \min_{\theta_{x}\in \Theta_{n_{x}},\theta_{y}\in \Theta_{n_{y}},\theta_{z}\in \Theta_{n_{z}}}L(R_{\theta_{x},\theta_{y},\theta_{z}}^{*},R)$ .
+
+However, due to the singularity of Euler angles [13], we can simplify the above rotation transformation. The pseudo-code implementation of our Symmetry-Aware Evaluation is provided in Algorithm 1. It allows us to simplify what was originally at most  $360^{3}$  computations to a maximum of only  $4^{3}$  computations.
+
+# 4.3 Large-Vocabulary 6D Pose and Size Estimation
+
+Performance on Omni6D. We present results of algorithms [6,10,34,46,47] trained on Omni6Dtrain and tested on Omni6Dtest. We compare their quantitative results in Tab. 2 and their qualitative results in Fig. S10 in Appendix. The performance disparity among algorithms for category-level 6D object pose estimation becomes markedly pronounced when applied to large-vocabulary datasets, in contrast to the more consistent performance previously observed on the Real and CAMERA datasets [40]. This highlights the inherent strengths and weaknesses across various model structures.
+
+This observation suggests the potential importance of our large-vocabulary dataset in uncovering the relative performance of different models. It appears
+
+Table 2: Category-level performance on Omni6D dataset. Models are trained on Omni6Dtrain and tested on Omni6Dtest. Instances within each category in the test set are unseen during training, substantiating the algorithms' capacity to generalize within individual categories under large-vocabulary settings. Bold and underlined results indicate the best and second-best performers.  
+
+<table><tr><td>Methods</td><td>Network</td><td>IoU50</td><td>IoU75</td><td>5°2cm</td><td>5°5cm</td><td>10°2cm</td><td>10°5cm</td><td>5°</td><td>10°</td><td>2cm</td><td>5cm</td></tr><tr><td>SPD [34]</td><td>implicit</td><td>44.56</td><td>20.37</td><td>7.55</td><td>9.56</td><td>14.76</td><td>19.23</td><td>10.68</td><td>21.02</td><td>37.49</td><td>70.09</td></tr><tr><td>SGPA [6]</td><td>implicit</td><td>36.34</td><td>14.44</td><td>4.78</td><td>6.84</td><td>10.13</td><td>15.03</td><td>8.49</td><td>17.73</td><td>25.57</td><td>59.18</td></tr><tr><td>DualPoseNet [20]</td><td>hybrid</td><td>58.84</td><td>25.49</td><td>8.28</td><td>9.30</td><td>17.26</td><td>19.05</td><td>9.38</td><td>19.18</td><td>73.82</td><td>96.37</td></tr><tr><td>RBP-Pose [46]</td><td>hybrid</td><td>35.92</td><td>4.66</td><td>0.37</td><td>0.60</td><td>0.53</td><td>0.80</td><td>0.75</td><td>0.96</td><td>39.73</td><td>83.55</td></tr><tr><td>GPV-Pose [10]</td><td>explicit</td><td>15.28</td><td>0.26</td><td>0.10</td><td>0.70</td><td>0.14</td><td>0.96</td><td>2.25</td><td>2.96</td><td>5.31</td><td>33.70</td></tr><tr><td>HS-Pose [47]</td><td>explicit</td><td>62.65</td><td>23.02</td><td>4.26</td><td>4.85</td><td>10.49</td><td>11.61</td><td>4.96</td><td>11.75</td><td>80.93</td><td>97.78</td></tr></table>
+
+Table 3: Category-level performance on unseen categories. Models are trained on Omni6Dtrain and tested on Omni6Dout. Categories in the test set never appear in the training set, validating the algorithms' ability to generalize across categories.  
+
+<table><tr><td>Methods</td><td>Network</td><td>IoU50</td><td>IoU75</td><td>5°2cm</td><td>5°5cm</td><td>10°2cm</td><td>10°5cm</td><td>5°</td><td>10°</td><td>2cm</td><td>5cm</td></tr><tr><td>SPD [34]</td><td>implicit</td><td>7.56</td><td>0.95</td><td>0.18</td><td>0.40</td><td>0.80</td><td>1.65</td><td>0.65</td><td>2.36</td><td>8.88</td><td>40.59</td></tr><tr><td>SGPA [6]</td><td>implicit</td><td>7.05</td><td>0.60</td><td>0.07</td><td>0.28</td><td>0.19</td><td>0.82</td><td>0.53</td><td>1.69</td><td>3.87</td><td>28.28</td></tr><tr><td>DualPoseNet [20]</td><td>hybrid</td><td>36.85</td><td>12.06</td><td>3.24</td><td>3.37</td><td>8.04</td><td>8.51</td><td>3.39</td><td>8.64</td><td>78.00</td><td>98.60</td></tr><tr><td>RBP-Pose [46]</td><td>hybrid</td><td>26.18</td><td>1.95</td><td>0.01</td><td>0.02</td><td>0.02</td><td>0.03</td><td>0.02</td><td>0.03</td><td>16.74</td><td>43.06</td></tr><tr><td>GPV-Pose [10]</td><td>explicit</td><td>10.97</td><td>0.14</td><td>0.03</td><td>0.18</td><td>0.12</td><td>0.57</td><td>0.30</td><td>1.07</td><td>7.14</td><td>41.30</td></tr><tr><td>HS-Pose [47]</td><td>explicit</td><td>36.75</td><td>8.92</td><td>1.54</td><td>1.66</td><td>4.67</td><td>5.16</td><td>1.75</td><td>5.38</td><td>79.95</td><td>98.27</td></tr></table>
+
+that the increased complexity of the dataset could push model architectures to their theoretical limits, possibly revealing intrinsic characteristics otherwise obscured in less complex scenarios. For example, SPD, SGPA is particularly proficient in predicting rotation, and SPD achieves the highest score in  $n^{\circ}mcm$ . This could be due to its implicit network's propensity for generating more reliable rotational forecasts. Meanwhile, DualPoseNet and HS-Pose provide more accurate predictions for translation and score higher in IoU. This could be associated with the characteristic of models with explicit networks to produce better translations and size estimates.
+
+Our large-vocabulary dataset, encompassing a broad spectrum of shapes and appearances, enables a comprehensive evaluation of diverse category-level pose estimation methods. This serves not only as a robust test of an algorithm's generalizability but also as a valuable tool in understanding the advantages offered by different algorithmic structures.
+
+Generalization Performance. We evaluate algorithms on  $\mathrm{Omni6D}_{out}$  to assess their inter-category generalization capabilities. The outcomes are presented in Tab. 3. Notably, DualPoseNet and HS-Pose emerged as superior performers, outclassing others across all metrics, thereby demonstrating excellent generalization abilities. Contrastingly, implicit methods including SPD and SPGA exhibited marked limitations. Qualitative results are shown in Fig. S11 in Appendix.
+
+Drawing parallels with the observations from Tab. 2, we found that metrics such as translation and IoU were relatively easier to excel in, suggesting superior
+
+![](images/4570cd25a0b82c7cbb1192ae3e87459036b14737d54672c088e5deb7b36383b5.jpg)  
+Fig. 5: Category-Wise Performance on Omni6D Dataset. The x-axis, moving from left to right, sequentially represents: the number of objects within a category (Semantic Category), the number of objects within a cluster clustered based on shape priors (Shape Category) and the diversity of instances within a category. The y-axis depicts category or clustered group results for IoU<sub>75</sub> and 5°2 cm metrics. Each plotted point illustrates the algorithm's result for a specific category or cluster, while the line showcases the trend of the linear fit for the scattered points.
+
+generalization abilities in translation and size prediction. Conversely, the generalization of rotation emerges as a considerable challenge in category-level 6D object pose estimation, especially within large-vocabulary scenes.
+
+Category-wise Analysis. Based on the  $\mathrm{IoU}_{75}$  and  $5^{\circ}2$  cm metrics, we conducted a detailed category-wise analysis of the results from Tab. 2. Left columns in Fig. 5 illustrate the correlation between category-level 6D pose estimation performance and the number of instances within each category in Omni6Dtrain. Middle columns in Fig. 5 analyze the correlation between cluster-level average performance and cluster size based on the clustering results described in Fig. 3e. We found that the performance of pose estimation for each category is more strongly correlated with the number of instances within clusters than with semantic categories, showing a positive correlation. This suggests that shape categories have a greater impact on training than semantic categories do. Notably, algorithms like SPD, SGPA, and RBP-Pose that utilize shape prior structures are particularly sensitive to this influence.
+
+Right columns in Fig. 5 reveal the correlation of pose estimation performance relative to instance diversity within each category in the training set. We measured instance diversity by calculating the mean chamfer distance [1] among all pairs of instances in each category. The results show that as diversity within a category increases, pose estimation performance tends to improve. This observation aligns with the assertion made by [23]: The key to the success of prior-based methods lies in the deformation modules, which learns to synthesize world-space target objects and explicitly builds the correspondence between camera and world-space. As the number of instances increases and the diversity within a shape category expands, the model's capacity to learn deformation from priors to actual instance shapes is strengthened, leading to improved results.
+
+![](images/c85a544023dec18ffe03f680575a37364787a74cbda9b5523875a42459d5637a.jpg)  
+(a) Category inventory of clsn
+
+![](images/e3ef99cf02c9d254bf358461b49c61e658fc66d4a7ae50b861cceaee9b6b7f9d.jpg)  
+(b) Our finetune strategy  
+Fig.6: Our finetune strategy. (a) Category inventory of c1sn within Omni6D dataset. The angle of each sector in the chart reflects the relative size of the instance count within that category. (b) In each fine-tuning step, we double the category count, copying trained global features and old category parameters into the new network while initializing the new category parameters. An observable deepening of color is indicative of the escalating count of training iterations.
+
+# 4.4 Fine-Tuning from Limited Categories
+
+We propose a finetuning strategy that helps extend methods from a limited set of categories to large-vocabulary. We take SPD [34], DualPoseNet [20], and HS-Pose [47] as examples which belong to three different network architectures and show good performance on Omni6D $_{test}$ . We respectively take their best models on CAMERA as our pre-trained models.
+
+Initiating the fine-tuning process, we utilize three categories: bottle, bowl, and cup, which are concurrently present in both Omni6D and CAMERA datasets, aligning with the cls3 category. By facilitating the training on Omni6D-cls3, we enable a transfer of the model from CAMERA to Omni6D. Following the method illustrated in Fig. 6b, we engage in an iterative fine-tuning process on a progressively expanded category dataset until it reaches our desired number. In our experiments, we set this target number to be 48 categories.
+
+In parallel, we conduct training from scratch separately on cls3, cls6, ..., and cls48 as a comparison, employing the same number of training iterations. As shown in Fig. 7, even with an exponential increase in the number of categories, pre-trained models remain pivotal in our fine-tuning strategy. The performance of fine-tuning consistently outperforms that of training from scratch.
+
+However, regardless of whether the training approach is finetuning or training from scratch, a decline in performance is observed as the number of categories increases. The decline rates for SPD and DualPoseNet are slower, coupled with an initial augmentation in performance due to increased training data and iterations. In contrast, HS-Pose experiences a more rapid decline, with fine-tuned  $5^{\circ}2$  cm results dropping from initial  $62.52\%$  to  $14.42\%$ . Models that excel in tasks involving a limited number of categories may not necessarily maintain their superiority in large-vocabulary tasks, they might be surpassed by models that are more robust and easier to train.
+
+![](images/ba9ac324d8671bccc8c28c4b9fcfa9dcde22967901e28bfec40073491d65830a.jpg)  
+Fig. 7: Finetuned results. Each figure's x-axis represents the number of categories in the training and test set, while the y-axis displays the outcomes of  $5^{\circ}2$  cm,  $5^{\circ}$  and  $2cm$  metrics. Each row, from top to bottom, sequentially employs three methods: SPD [34], DualPoseNet [20], and HS-Pose [47]. The figures depict the outcomes derived from two training strategies as the number of training categories increases, accompanied by the gradual expansion of corresponding test sets.
+
+# 4.5 Visual Realism
+
+Due to the complexity of collecting and annotating real-world data, contemporary datasets like NOCS [40] are composed of a large amount of synthetic data and a small portion of real-world data. While collecting real data is relatively straightforward when the number of categories is limited, gathering well-annotated real-world data for pose estimation tasks involving large vocabulary categories becomes a monumental task.
+
+Our Omni6D dataset, which includes large vocabulary objects, is also derived from rendering. However, the incorporation of real-scanned objects significantly enhances the realism of the rendered images. As depicted in Fig. 8, Omni6D receives a score of  $2.69 \pm 0.39$ , surpassing the results obtained by CAMERA.
+
+Given these significant advantages, our dataset excels not only in large-vocabulary scenarios but also in real-world scenes. As depicted in Tab. 4, We use DualPoseNet [20] to train on the common categories in REAL [40] and Omni6D, namely bottles, bowls, and mugs. We train separately on the two datasets and their mix. The results show that Omni6D models perform well on REAL275, and training on the mixed dataset outperforms using REAL or Omni6D datasets alone. This demonstrates that our dataset enables the direct transfer of models to real-world scenes. Moreover, it seamlessly supplements the existing real-world dataset, enabling joint training of models on our dataset and the real-world data.
+
+To further validate the sim2real capability of models trained with Omni6D, we constructed a real-world dataset, Omni6D-Real, comprising 30 scenes, 39 categories, 73 instances, and 1k images. We captured RGBD images with Azure
+
+![](images/38cafe713d1ef714db0a8789a5bbedf739e0033b567d6dbaa80f835060781305.jpg)  
+Omni6D  $2.69\pm 0.39$
+
+![](images/55df81ccc0ce4ee128d7ec79f7423764222c93e70de8753013ec6fbeee0fe8d8.jpg)  
+CAMERA  $1.55\pm 0.08$  
+Fig. 8: Comparison of Visual Realism. We evaluated the visual realism of Omni6D in comparison to other datasets through a survey involving 70 human subjects. We randomly selected 10 images from each dataset and introduced noise by blending in 5 images from COCO [21], which included captured photos, and  $\mathrm{SKETCH}^4$ , which comprised rendered images. Subjects were asked to rate the realism of sampled images on a scale from 1 (least realistic) to 5 (most realistic). We report the mean and standard deviation and include a sampled image from the study.
+
+![](images/7b9cffb67d1dbd3750ea2af15078b90bead68e931b3c33ee7a8e542584b0f8ab.jpg)  
+REAL  $3.53\pm 0.28$
+
+![](images/f37437a1c1f431216714574d7ee1872c7966b598f3e50fea098885e97b7140f2.jpg)  
+WILD6D  $4.38\pm 0.23$
+
+Table 4: Performance on REAL275 with Different Training Sets. It compares how different training sets influence DualPoseNet's performance on REAL275 [40], providing insights into the model's ability to generalize in real-world tasks using Omni6D.  
+
+<table><tr><td>Train data</td><td>Realism</td><td>IoU50</td><td>IoU75</td><td>5°2cm</td><td>5°5cm</td><td>10°2cm</td><td>10°5cm</td><td>5°</td><td>2cm</td></tr><tr><td>Omni6D</td><td>Real-Scanned</td><td>78.76</td><td>32.69</td><td>6.55</td><td>8.80</td><td>15.00</td><td>21.38</td><td>11.20</td><td>49.54</td></tr><tr><td>REAL [40]</td><td>Real</td><td>84.51</td><td>43.43</td><td>8.76</td><td>10.40</td><td>21.24</td><td>25.39</td><td>13.01</td><td>69.46</td></tr><tr><td>REAL+Omni6D</td><td>Mixed</td><td>85.28</td><td>58.59</td><td>14.10</td><td>17.83</td><td>30.10</td><td>38.97</td><td>20.96</td><td>71.00</td></tr></table>
+
+Kinect  $\mathrm{DK}^3$  and preprocessed them using SAM [18] for object masks and ICP [2] for point cloud registration. Details are provided in Appendix Section D.
+
+# 5 Conclusion
+
+In conclusion, this paper introduces Omni6D, a novel 6D pose estimation dataset with large-vocabulary categories and intricate scenes. We evaluate existing category-level 6D object pose estimation methods on this benchmark, analyze its challenges, and propose a fine-tuning strategy for large-vocabulary scenarios. Limitations. Our dataset, though more complex, doesn't fully encompass all real-world challenges. Additionally, our fine-tuning strategy effectively extends methods from a small set to a larger one, but its efficacy may decrease with growing category diversity.
+
+Future Work. Our study paves the way for diverse research avenues. An immediate next step is expanding the Omni6D dataset with more object types and scenes for comprehensive coverage. Additionally, annotating videos for scanned objects will validate algorithms' large-vocab pose estimation in real-world scenarios. Designing new training strategies for coping with increasing category diversity presents an intriguing challenge.
+
+# Acknowledgements
+
+This research is supported by Shanghai Artificial Intelligence Laboratory, the Centre for Perceptual and Interactive Intelligence (CPII) Ltd under the Innovation and Technology Commission (ITC)'s InnoHK, the Ministry of Education, Singapore, under its MOE AcRF Tier 2 (MOET2EP20221-0012), NTU NAP, and under the RIE2020 Industry Alignment Fund - Industry Collaboration Projects (IAF-ICP) Funding Initiative.
+
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+    {
+        "type": "text",
+        "text": "pyautogui.moveTo(210, 179)  \npyautoguidragTo(243, 179, button=\"left\")",
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+        "text": "pyautogui.click(398,167) pyautogui.write(\"Seattle,WA\") pyautogui.write(\"enter\")",
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+        "text": "import pyautogui",
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+        "text": "pyautogui.goiveFo(410, 207)  \npyautogui.hscroll(10)  \npyautogui.clclick(410, 207)",
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+        "text": "Illustration",
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+        "type": "text",
+        "text": "Abstract. For decades, human-computer interaction has fundamentally been manual. Even today, almost all productive work done on the computer necessitates human input at every step. Autonomous virtual agents represent an exciting step in automating many of these menial tasks. Virtual agents would empower users with limited technical proficiency to harness the full possibilities of computer systems. They could also enable the efficient streamlining of numerous computer tasks, ranging from calendar management to complex travel bookings, with minimal human intervention. In this paper, we introduce OmniACT, the first-of-a-kind",
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+        "type": "page_footnote",
+        "text": "* These authors contributed equally. The order is determined by dice rolling.",
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+    {
+        "type": "text",
+        "text": "dataset and benchmark for assessing an agent's capability to generate executable programs to accomplish computer tasks. Our scope extends beyond traditional web automation, covering a diverse range of desktop applications. The dataset consists of fundamental tasks such as \"Play the next song\", as well as longer horizon tasks such as \"Send an email to John Doe mentioning the time and place to meet\". Specifically, given a pair of screen image and a visually-grounded natural language task, the goal is to generate a script capable of fully executing the task. We run several strong baseline language model agents on our benchmark. The strongest baseline, GPT-4, performs the best on our benchmark. However, its performance level still reaches only  $15\\%$  of the human proficiency in generating executable scripts capable of completing the task, demonstrating the challenge of our task for conventional web agents. Our benchmark provides a platform to measure and evaluate the progress of language model agents in automating computer tasks and motivates future work towards building multimodal models that bridge large language models and the visual grounding of computer screens.",
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+    {
+        "type": "text",
+        "text": "Keywords: Generalist Agent  $\\cdot$  Multimodal Machine Learning  $\\cdot$  Dataset and Benchmark  $\\cdot$  Vision Language Understanding  $\\cdot$  UI grounding  $\\cdot$  Human-computer interaction",
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+    {
+        "type": "text",
+        "text": "1 Introduction",
+        "text_level": 1,
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+        "type": "text",
+        "text": "Performing computer tasks based on natural language instructions has been a long-standing goal of artificial intelligence [49]. One concrete objective in the line of research is to develop generalist agents that can assist humans in doing computer tasks [21], such as \"Order a pizza from Domino's\" or \"Write a message to John.\" The agent should be able to open the application and perform the task. Executing these actions on a personal computer involves a sequence of interactions with a mouse and keyboard. For example, the simple task of writing an email involves hovering over the application icon, clicking it, clicking the 'New Email' button, writing the content of the email, and clicking send. Successfully sending an email requires accurately predicting the correct action at each step and accurately executing it, which is a herculean task even for the best agents today [14].",
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+    {
+        "type": "text",
+        "text": "A generalist agent for computer tasks must understand natural language instructions, process visual screenshots, and produce the correct sequence of actions to be performed to achieve the intended task. Several existing approaches focus on building agents based on the HTML model [9, 40, 62]. However, this approach introduces several challenges and constraints. These agents are limited to web applications and often struggle with complex or long-context HTML code. They cannot interact with native desktop applications or perform tasks that span multiple applications, like drafting an email using text from a code editor, without significant alterations. Furthermore, HTML-based agents, which are inherently powered by text-only language models, typically underperform in tasks requiring visual cues, such as identifying and clicking a blue button",
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+    {
+        "type": "page_number",
+        "text": "2",
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+    {
+        "type": "header",
+        "text": "R. Kapoor, Y. Butala et al.",
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+    },
+    {
+        "type": "text",
+        "text": "on a desktop's top-right corner. In contrast, humans can easily understand UI elements like dropdown menus, typable areas, redirections, and options with just a glance.",
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+    {
+        "type": "text",
+        "text": "Towards the goal of developing a generalist autonomous agent with robust visual and user interface (UI) understanding capabilities, we introduce a new task and dataset, OmniACT, containing over 9.8K pairs of images and instructions (Figure 1) across different operating systems and the web. This dataset includes screenshots of various UI screens and corresponding natural language instructions. The objective of these instructions is to generate executable commands using the PyAutoGUI Python library [1]. PyAutoGUI enables the automation of the mouse and keyboard operations, which helps to facilitate interactions with various native applications across macOS, Windows, and Linux. This simplifies completing specified tasks across different web domains and native desktop applications.",
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+    {
+        "type": "text",
+        "text": "We evaluate several language model-based agent baselines on this dataset, including LLaMA [47], Vicuna [7], Palmyra-X (43B) [2], InstructPalmyra-30B [45], GPT 3.5, and GPT-4 [32]. We experiment with fine-tuning Vicuna-13B and LLaMA-13B models using QLoRA [10]. We also benchmark multimodal baseline LLaVa-v1.5-7B, LLaVa-v1.5-13B [47], Gemini-Pro [44] and GPT-4-vision-preview [55] for the task. Our findings highlight the necessity for a multimodal model capable of executing these tasks, and our analysis provides insights into promising future work in the space. Our key contributions are outlined as follows:",
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+        "list_items": [
+            "1. We release a novel dataset of desktop and website applications consisting of over 9.8K natural language tasks, UI screens, and corresponding code snippets collected through human annotation. We introduce custom performance metrics tailored for computer tasks.",
+            "2. We propose DetACT, a module for creating textual representations of the screen using signals from OCR, color, and icon-template matching.",
+            "3. We conduct a comprehensive benchmark and analysis of state-of-the-art LLMs and multimodal models on our benchmark. Our results show that OmniACT is a challenging task for even the best LLM agents today, and existing models are far below human performance."
+        ],
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+    {
+        "type": "text",
+        "text": "2 Related Work",
+        "text_level": 1,
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+        "text": "2.1 UI Understanding",
+        "text_level": 1,
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+        "text": "User interface (UI) understanding has garnered interest from researchers in the machine learning and human-computer interaction communities, evolving with various models focusing on understanding the semantics of mobile and web user interfaces. UIBert [3], PixelBERT [16], ActionBert [15], VUT [25], Screen2Words [48], WidgetCaptioning [24] and Pix2Act [39] are notable models in this area. They propose approaches for learning the user-interface semantics of the mobile screen using the image and view hierarchy. These models have demonstrated effectiveness in tasks like capability prediction, screen segmentation and",
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+    {
+        "type": "header",
+        "text": "OmniACT",
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+    {
+        "type": "page_number",
+        "text": "3",
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+    {
+        "type": "table",
+        "img_path": "images/8d48eb443c512036321444e8686fc6fc5c06aad51b713709dd42daba7b1694ea.jpg",
+        "table_caption": [
+            "Table 1: Comparison of OmniACT with other related benchmarks."
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Datasets</td><td>Size</td><td>Env Type</td><td>Task Heterogeneity</td><td>Real-World Portrayal</td><td>Executional Correctness</td><td>Supports Continuous Scale Desktop Apps</td><td>Adaptive Evaluation</td><td>Task</td></tr><tr><td>VisualWebArena [20]</td><td>910</td><td>Web</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>Web Navigation</td></tr><tr><td>WebArena [62]</td><td>812</td><td>Web</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>Web Navigation</td></tr><tr><td>Mind2Web [9]</td><td>2350</td><td>Web</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td>Web Navigation</td></tr><tr><td>WebShop [56]</td><td>12000 Products</td><td>Web</td><td>No</td><td>No</td><td>Yes</td><td>No</td><td>No</td><td>Web Navigation</td></tr><tr><td>RUSS [53]</td><td>80</td><td>Web</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td>Web Navigation</td></tr><tr><td>WebSRC [6]</td><td>2735</td><td>Web</td><td>Yes</td><td>Yes</td><td>-</td><td>No</td><td>No</td><td>QA</td></tr><tr><td>MiniWoB++ [17]</td><td>100</td><td>Mobile Websites</td><td>No</td><td>No</td><td>Yes</td><td>No</td><td>No</td><td>Web Navigation</td></tr><tr><td>PixelHelp [23]</td><td>187</td><td>Mobile</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td>UI Grounding</td></tr><tr><td>MetaGUI [42]</td><td>1125</td><td>Mobile</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>Mobile Navigation</td></tr><tr><td>MoTIF [5]</td><td>756</td><td>Mobile</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>Mobile Navigation</td></tr><tr><td>AITW [35]</td><td>715142</td><td>Mobile and Web</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>Mobile/Web Navigation</td></tr><tr><td>OmniACT (Ours)</td><td>9802</td><td>Desktop and Web</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Code Generation</td></tr></table>",
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+    {
+        "type": "text",
+        "text": "understanding, and screen caption generation. Lexi [4] and Spotlight [22] propose models that use vision-only inputs to minimize the reliance on metadata such as view hierarchy. Furata et al. [11] demonstrates the use of fine-tuning for multimodal web navigation. The majority of machine learning models trained for UI understanding leverage the Rico dataset [8] and its extensions, which contain 64,462 unique Android screens and metadata. In addition, [4] released the UICaptions dataset, which consists of diverse image-captions pairs across a wide range of applications. PixelHelp [23] also released a corpus to train models that can interpret natural language instructions and map them to mobile UI actions.",
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+    {
+        "type": "text",
+        "text": "2.2 Autonomous Computer Agents",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "The advent of large language models (LLMs) has been pivotal in the rapid advancement of agents that operate on web pages. Recent research such as ViperGPT [43] Chameleon [29], RCI Agent [18], VisProg [12], and [31] employ LLMs for planning or action prediction in developing autonomous agents. Benchmark datasets, such as MiniWoB [40], WebShop [56], Macaw-LLM [30], ASH-Prompting [41] Mind2Web [9], WebArena [62], AgentBench [28] and VisualWebArena [20] have also been proposed to measure the ability of LLM-based agents to automate web tasks. These methods mainly involve agents that operate on a text-based Document Object Model (DOM) of HTML scripts. This limits their understanding of screen context, which is crucial for the model's decision-making and action-taking processes. To address this limitation, [35] released Android in the Wild, a dataset comprising screens, natural language instructions, and corresponding actions. Following this, [59] proposed a multimodal model, AutoUI, which is designed to build an agent on the Android in the Wild dataset confined to the Android ecosystem. WebAgent [13] utilized Flan-U-PaLM, for grounded code generation, and HTML-T5 and showed improvement on real-world websites.",
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+    {
+        "type": "text",
+        "text": "Current benchmarks for autonomous agents focus mainly on the Web or Android environments, posing challenges for tasks involving desktop applications or spanning multiple applications beyond the web domain. The absence of established benchmarks and datasets in this area, coupled with basic methods",
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+        "type": "page_number",
+        "text": "4",
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+    {
+        "type": "header",
+        "text": "R. Kapoor, Y. Butala et al.",
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+    {
+        "type": "text",
+        "text": "for extracting user interface (UI) elements, underscores the need for significant progress in developing more versatile autonomous agents capable of handling diverse tasks beyond the current scope. To highlight the unique features that OmniACT introduces in the assessment of capable autonomous agents, we provide a comparison between the existing benchmarks and our proposed benchmark, OmniACT, in Table 1.",
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+        "type": "image",
+        "img_path": "images/3637b64b82edc539bc8230362a244c2b1b77bf9d9c6277baaa4ab34339e3319e.jpg",
+        "image_caption": [
+            "Fig. 2: Data Collection Pipeline. (1) We select over 60 applications and websites to ensure diversity, (2) segment the screen through human-annotated bounding boxes, (3) label the bounding boxes based on functionality, (4) ask student volunteers to come up with tasks, given a screen image, and (5) reverse map the textual labels to coordinates and filter the scripts based on execution and syntax."
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+    {
+        "type": "text",
+        "text": "3 OmniACT",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "We introduce a novel dataset and benchmark, OmniACT, which measures the performance of autonomous agents on both web and desktop applications. Compared to previous benchmarks which focus on text-based reasoning [9, 17, 40, 56, 62], our benchmark aims to measure multimodal agents that bridge large language model planners and UI understanding vision models. OmniACT can be accomplished as a standalone task as it is not under a mock environment.",
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+    {
+        "type": "text",
+        "text": "All actions that a human can execute on the computer can be encoded in the PyAutoGUI [1] Python framework. This framework allows a user to execute keyboard and mouse operations by running Python code. The PyAutoGUI code to execute these tasks is shown in the third column of Figure 1. For other computer tasks, the PyAutoGUI library provides functions such as 'press', 'write', and 'scroll' which can be used to execute the task. Our dataset consists of parallel data of natural language tasks, UI screenshots, and ground truth PyAutoGUI scripts that achieve successful execution.",
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+        "type": "header",
+        "text": "OmniACT",
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+    {
+        "type": "page_number",
+        "text": "5",
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+    {
+        "type": "text",
+        "text": "3.1 Task Formulation",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "Given an input state of a computer defined by the screen  $S$  and the task description  $T$  in natural language, the goal of the task is to output a sequence of actions  $A$  that can successfully accomplish the task  $T$  within a screenshot  $S \\in \\{ \\text{Linux, Windows, MacOS, Webpage} \\}$ . Formally, the task can be defined as learning the transition function  $f: T \\times S \\to A$ . During dataset collection, we ensure that all task descriptions  $T$  are feasible and can be accomplished in the current screenshot  $S$ . To reduce ambiguity and facilitate better evaluation, we ensure that task descriptions are detailed and unambiguous. Tasks can also be visually grounded (e.g., 'Click the red button to start recording') or natural language based (e.g., 'Click the My Account button'). We define the action space using the functionalities in the PyAutoGUI library:  $A \\in \\{\\text{'click', 'dragTo', 'scroll', 'write', \\ldots}\\}$ . The exhaustive list of actions is provided in Table 2. Our action space is much larger than other benchmarks [9,40,62] that resort to two or three interaction options. Mouse actions such as 'moveTo', 'click', 'rightClick', 'doubleClick', and 'dragTo', additionally require screen coordinates as arguments, which indicate the pixel location of the action.",
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+    {
+        "type": "text",
+        "text": "Figure 1 illustrates sample tasks and corresponding outputs for three applications within OmniACT: (1) Stocks (MacOS), (2) Apartments.com (web page), and (3) Weather (MacOS). The first column depicts the input image, and the second column shows the natural language task that is to be executed on the current screen. To execute these tasks, a user must accurately perform a series of operations using the mouse and keyboard. Eg: to check the rate of change in Google's stock price over the last month, the mouse has to be moved to the last month and dragged while holding the left-click button to the current month.",
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+    {
+        "type": "text",
+        "text": "3.2 Dataset Preparation",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "To prepare our dataset, we followed a pipelined approach, as summarized in Figure 2. We first selected a variety of applications and websites. For each application or website, we created bounding boxes around key UI elements and labeled them according to their functionality, which is crucial for assisting human annotators in writing accurate PyAutoGUI scripts. After each script is written, we converted the labels back into numeric coordinates, allowing us to align the scripts precisely with the locations of the UI elements. Finally, we thoroughly reviewed each script, focusing on its executability and adherence to syntax standards. This ensured the high quality and functionality of our dataset, making it a valuable resource for training and evaluating autonomous agents.",
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+    {
+        "type": "text",
+        "text": "Application/Website Selection To test the computer agents' generalization ability across different tasks, we collect tasks across multiple domains on both desktop and web applications. In total, we collect and annotate 9802 data points (Table 3), with the split between desktop and web applications approximately 3:1. The emphasis on desktop applications, which do not contain Document Object Model (DOM) hierarchies unlike HTML-based web pages, presents a more",
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+        "text": "6",
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+        "type": "header",
+        "text": "R. Kapoor, Y. Butala et al.",
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+    {
+        "type": "text",
+        "text": "complex multimodal challenge where visual cues are crucial. We collect tasks from applications within the three most popular operating systems. We select 22 native applications from MacOS, and 8 each from Linux and Windows. We annotate roughly 3 to 4 screens for every application. The full list of applications is provided in the Appendix.",
+        "bbox": [
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+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "Many common computer tasks today are still performed through web applications, so we also collect 3-4 screenshots from 27 different web applications. To ensure diversity in task intents, we categorize these tasks into one of the following 6 categories: (1) Shopping, (2) Entertainment, (3) Service, (4) Government, (5) Travel, (6) Health. Inspired by the methodology of [9], these categories were selected to cover a wide range of user intents and functionalities.",
+        "bbox": [
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+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "table",
+        "img_path": "images/efd3839d384fbecee73b13876012435c71bbd9d96d15ee0c14640fba843ab165.jpg",
+        "table_caption": [
+            "Table 2: Action types supported by OmniACT and the number of instances for each action in the dataset."
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Type</td><td>Action</td><td>%</td></tr><tr><td rowspan=\"7\">Mouse</td><td>Click</td><td>63.73</td></tr><tr><td>Double Click</td><td>0.58</td></tr><tr><td>Right Click</td><td>0.77</td></tr><tr><td>Move/Hover</td><td>1.85</td></tr><tr><td>Drag</td><td>0.29</td></tr><tr><td>Scroll</td><td>1.68</td></tr><tr><td>Horizontal Scroll</td><td>0.17</td></tr><tr><td rowspan=\"3\">Keyboard</td><td>Press</td><td>16.28</td></tr><tr><td>Hotkey</td><td>3.00</td></tr><tr><td>Write</td><td>11.65</td></tr></table>",
+        "bbox": [
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+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "UI Screen Segmentation To collect gold-standard data, we first annotate and segment the screen by identifying the",
+        "bbox": [
+            212,
+            425,
+            514,
+            470
+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "bounding boxes present on the screen. We employ slightly different techniques for web and desktop applications to create the bounding boxes:",
+        "bbox": [
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+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "list",
+        "sub_type": "text",
+        "list_items": [
+            "1. Desktop Applications: We build a custom annotation interface based on PyQt5 $^3$  to create bounding boxes manually over a screen image using a simple drag-and-click mechanism. This custom interface expedites the process and allows us to get highly accurate gold-label data for desktop images.",
+            "2. Websites: For webpages, we write JavaScript code to extract all interactable (click, type, etc.) regions from HTML source code. We also extract banners, dropowns, submit, and radio buttons from the screen. We filter the elements to retain only those that are visible and interactable within the screen."
+        ],
+        "bbox": [
+            220,
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+            633
+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "Functionality Tagging To map each bounding box to its correct functional description, we leverage Amazon MTurk workers (see details in Appendix), who are given an image with a bounding box and are required to write the correct description or label of the bounding box's function. For example, given an image of an Amazon webpage with a search bar, the annotator labels it as \"find-product-search-bar\". The logical descriptions are used to create tasks in a structured manner without the need to identify individual bounding box coordinates.",
+        "bbox": [
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+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "Task Creation Our approach for each screen involves utilizing all human-annotated bounding boxes and their labels to create tasks that can be executed",
+        "bbox": [
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+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "header",
+        "text": "OmniACT",
+        "bbox": [
+            658,
+            114,
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+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "page_number",
+        "text": "7",
+        "bbox": [
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+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "page_footnote",
+        "text": "3 https://pypi.org/project/PyQt5/",
+        "bbox": [
+            217,
+            823,
+            472,
+            839
+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "image",
+        "img_path": "images/91bc6bf190024aeb93b022e0af17e277beeacd4e3c1c9b2f438622bc2f6c41f0.jpg",
+        "image_caption": [
+            "Fig. 3: DetACT Module. Given an initial image and a natural language task description, we use a pipelined approach to run OCR and SAM on the screen. The outputs from SAM are then used by icon and color-matching modules to obtain an exhaustive set of useful UI elements. The list of elements is passed through LLM based filter to select only the elements related to the given task."
+        ],
+        "image_footnote": [],
+        "bbox": [
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+            143,
+            782,
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+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "within the confines of a single screen. These tasks are designed to be visually grounded in order to measure the capabilities of multimodal agents. We plan to release the bounding box and their corresponding labels as the metadata for evaluation purposes.",
+        "bbox": [
+            212,
+            436,
+            784,
+            497
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "For dataset compilation, college students with basic Python programming skills served as annotators, accessing API references for PyAutoGUI and examples of potential tasks. Each student generated multiple tasks, each accompanied by three alternative natural language reformulations. For instance, \"What is  $3 + 2$ ?\" might be reformulated as \"Calculate the sum of 2 and  $3$ \" or \"Add two to three\". To avoid train-test leakage, rephrased tasks were consistently placed in the same dataset split. Further details on the annotation process are available in the Appendix.",
+        "bbox": [
+            212,
+            498,
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+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "Reverse Mapping and Filtering To ensure high-quality data, we incorporate an additional step into the data collection pipeline. We build scripts to map the text-based labels of each bounding box back to their numeric coordinates, and then match the syntax and verify if the task will be executed on the screen. Using this filter, we remove all the non-working or syntactically incorrect data points and finally manually review the set of tasks.",
+        "bbox": [
+            212,
+            642,
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+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "After filtering, we obtain 9802 human-annotated, gold-label data points across more than 200 desktop and web screens",
+        "bbox": [
+            212,
+            809,
+            785,
+            840
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "table",
+        "img_path": "images/3ff6e279b9f9ca02e4e290ee118f367f93872ab2d707191e6b54439e215e34e7.jpg",
+        "table_caption": [
+            "Table 3: Dataset distribution across splits and platforms."
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td colspan=\"2\">Domain</td><td>Train Validation</td><td>Test</td><td>Total</td></tr><tr><td rowspan=\"3\">Desktop</td><td>Mac OS</td><td>3028</td><td>444</td><td>786</td></tr><tr><td>Linux</td><td>761</td><td>126</td><td>247</td></tr><tr><td>Windows</td><td>1573</td><td>216</td><td>458</td></tr><tr><td>Web</td><td>-</td><td>1427</td><td>206</td><td>530</td></tr><tr><td>Total</td><td></td><td>6789</td><td>992</td><td>2,021</td></tr></table>",
+        "bbox": [
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+        "page_idx": 7
+    },
+    {
+        "type": "page_number",
+        "text": "8",
+        "bbox": [
+            217,
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+            228,
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+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "header",
+        "text": "R. Kapoor, Y. Butala et al.",
+        "bbox": [
+            271,
+            114,
+            455,
+            128
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "(Table 3), split into train, validation, and test sets in a 7:1:2 ratio. All collected data will be publicly released to encourage future work on multimodal agents.",
+        "bbox": [
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+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "4 Evaluation Metrics",
+        "text_level": 1,
+        "bbox": [
+            215,
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+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "In this section, we detail various evaluation metrics for benchmarking model performance on the OmniACT dataset. UI screens have additional constraints such as spatial relevance which are not factored in most conventional similarity-based metrics such as BLEU [34], CodeBLEU [36], BERTScore [58] and CodeBERTScore [61]. For example, a valid click action is usually not constrained to a single coordinate but can be any coordinate within a specified region. In the event of invalid coordinate predictions, an agent that predicts coordinates further away from the valid region should invoke a higher penalty compared to an agent that predicted coordinates close to the region. We propose two new metrics adapted: Sequence Score (Section 4.1) and Action Score (Section 4.2) aimed at utilizing UI information.",
+        "bbox": [
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+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "4.1 Sequence Score",
+        "text_level": 1,
+        "bbox": [
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+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "The sequence score measures whether the predicted action sequence (e.g., 'click', 'write', 'press') exactly matches the gold sequence. Since predicting the first action in the sequence is relatively straightforward and later actions are more difficult, we define sequence score as follows:",
+        "bbox": [
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+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\n\\operatorname {S e q S c o r e} _ {i} = \\left\\{ \\begin{array}{l l} \\beta_ {1} + \\beta_ {2} * (s - 1) & \\text {i f a l l a c t i o n s m a t c h} \\\\ 0 & \\text {o t h e r w i s e} \\end{array} \\right. \\tag {1}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
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+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "where  $s$  is the action sequence length,  $\\beta_{1}$  is set to 0.1 and  $\\beta_{2}$  is set to 1.",
+        "bbox": [
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+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "4.2 Action Score",
+        "text_level": 1,
+        "bbox": [
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+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "The action score measures how well a code snippet containing the correct action sequence can perform the task. Specifically, for a script with a correct action sequence, we introduce penalties for inaccurate behavior. The penalties are described below:",
+        "bbox": [
+            212,
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+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "1. Click penalty  $(M)$ : For actions 'click', 'rightClick', 'doubleClick', 'moveTo', and 'dragTo', we penalize code snippets where predicted coordinates lie outside of the bounding box of the UI element. The click penalty for the  $j^{th}$  action of the  $i^{th}$  example is defined as:",
+        "bbox": [
+            222,
+            681,
+            782,
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+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\nM _ {i} ^ {j} = \\alpha_ {i} \\times \\left\\{ \\begin{array}{l l} 1 - \\frac {\\mu}{\\mu + L _ {2}} & \\text {i f S e q S c o r e} _ {i} > 0 \\\\ 1 & \\text {o t h e r w i s e} \\end{array} \\right. \\tag {2}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
+            357,
+            744,
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+            792
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "Here  $L_{2}$  corresponds to the smallest Euclidean distance between the predicted coordinate and bounding box.  $L_{2}$  is zero when the predicted coordinate lies within the target bounding box.  $\\mu$  is the Dirichlet smoothing",
+        "bbox": [
+            241,
+            794,
+            784,
+            840
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "header",
+        "text": "OmniACT",
+        "bbox": [
+            658,
+            114,
+            730,
+            126
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "page_number",
+        "text": "9",
+        "bbox": [
+            774,
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+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "coefficient which we dynamically set to the inverse of the length of the diagonal of the bounding box. This ensures that the penalty for points outside the bounding box varies based on the size of the bounding box. For two predicted points with the same  $L_{2}$ , the metric penalizes more heavily if the box is larger. This is sound with the intuition that the chances of clicking on a larger box are higher and should be penalized more in case of a mistake.",
+        "bbox": [
+            240,
+            146,
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+            236
+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "2. Key penalty  $(K)$ : For actions 'press' and 'hotkey', we check whether the set of keys in the target code (represented as  $GK_{i}^{j}$ ) and predicted code (represented as  $PK_{i}^{j}$ ) are the same. It is formally defined as:",
+        "bbox": [
+            222,
+            237,
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+            284
+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\nK _ {i} ^ {j} = \\alpha_ {i} \\times \\left\\{ \\begin{array}{l l} 0 & \\text {i f} G K _ {i} ^ {j} = P K _ {i} ^ {j} \\text {a n d S e q S c o r e} _ {i} > 0 \\\\ 1 & \\text {o t h e r w i s e} \\end{array} \\right. \\tag {3}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
+            330,
+            287,
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+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "3. Write penalty  $(W_{p})$ : For action type 'write', we penalize the output for the sentence to be typed. Specifically, we the employ BLEU score [34], and compute:",
+        "bbox": [
+            222,
+            329,
+            785,
+            375
+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\nW _ {i} ^ {j} = \\alpha_ {i} \\times \\left\\{ \\begin{array}{l l} 1 - B L E U \\left(G S _ {i} ^ {j}, P S _ {i} ^ {j}\\right) & \\text {i f S e q S c o r e} _ {i} > 1 \\\\ 1 & \\text {o t h e r w i s e} \\end{array} \\right. \\tag {4}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
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+            377,
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+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "Here,  $GS_{i}^{j}$  represents the actual sentence to be typed, and  $PS_{i}^{j}$  represents the sentence predicted by the model in the  $j^{th}$  action of example  $i$ .",
+        "bbox": [
+            241,
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+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "In the above equations,  $(\\alpha_{i})$  is the weighting factor:",
+        "bbox": [
+            215,
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+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\n\\alpha_ {i} = \\operatorname {S e q S c o r e} _ {i} / \\text {l e n g t h o f s e q u e n c e} i \\tag {5}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
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+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "This ensures that the action score  $\\in [0,1]$ . The mean action score is calculated as follows:",
+        "bbox": [
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+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\n\\text {A c t i o n S c o r e} = \\frac {\\sum_ {i} \\max  \\left(\\operatorname {S e q S c o r e} _ {i} - \\sum_ {j} \\left(M _ {i} ^ {j} + K _ {i} ^ {j} + W _ {i} ^ {j}\\right) , 0\\right)}{\\sum_ {i} \\operatorname {S e q S c o r e} _ {i}} \\tag {6}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
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+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "5 DetACT: DETecting ACTions from UI",
+        "text_level": 1,
+        "bbox": [
+            215,
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+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "Understanding UI screens is crucial for multimodal computer tasks. Web-based agents typically use language-only inputs from the HTML DOM. This is insufficient for comprehending the full extent of an application UI, as many components may not be easily described with HTML code. To address this, we propose DetACT, which allows us to convert images of UI layouts into structured code and text outputs for a downstream LLM. DetACT is a system comprised of three distinct modules: the text module, the icon module, and the color module.",
+        "bbox": [
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+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "1. Text Extraction: We use the EasyOCR model<sup>4</sup> to parse over the UI screens and collect all text-based elements. Along with the text, we also note the locations of each of these elements. This is depicted in Figure 3, along with a list of text elements found on the screen using the OCR Module. We segment and classify the different regions within the screenshot using the",
+        "bbox": [
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+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "page_number",
+        "text": "10",
+        "bbox": [
+            217,
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+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "header",
+        "text": "R. Kapoor, Y. Butala et al.",
+        "bbox": [
+            271,
+            114,
+            455,
+            128
+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "page_footnote",
+        "text": "4 https://github.com/JaidedAI/EasyOCR",
+        "bbox": [
+            218,
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+            506,
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+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "image",
+        "img_path": "images/877231d43634aee2879a1c206ac0fe7070408c08015ff5c3ce1a8c4b850a6958.jpg",
+        "image_caption": [
+            "Fig. 4: Baseline Model Architecture. Image and task descriptions are sent to DetACT module, which gives a filtered list of UI elements relevant to feed into the prompt along with the task. We also show the prompt structure used for action script generation. This structure is passed through the LLM (along with the image for multimodal LLM) to generate the automation script."
+        ],
+        "image_footnote": [],
+        "bbox": [
+            334,
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+        ],
+        "page_idx": 10
+    },
+    {
+        "type": "text",
+        "text": "Segment Anything Model (SAM) [19]. From the outputs, we filter out the non-textual segments for our icon and color detection.",
+        "bbox": [
+            240,
+            522,
+            784,
+            551
+        ],
+        "page_idx": 10
+    },
+    {
+        "type": "list",
+        "sub_type": "text",
+        "list_items": [
+            "2. Icon Module: For matching with the appropriate icon, we use a pack of 1600 icons<sup>5</sup> as templates. Each of these icons is labeled with their appropriate functionality and is matched with the filtered outputs SAM [19]. For the similarity of the two images, we resize the reference icons and segmented region of interest (ROI) to the same size, and convert both images to grayscale. After this, we use the Structural Similarity Index (SSIM) [52], to find the closest match of the ROI to the icons in our set, and select the ones above the SSIM threshold of 0.95. As seen in Figure 3, a few icons matched on the screen are Globe icon, Calendar icon, Person icon, and Location icon; each depicting a different use case.",
+            "3. Color Module: Finally, to place all segments of interest into appropriate buckets of colors, we average the RGB pixel values over the ROI and, based on that value, bucket them into different color categories. We categorize colors differently based on the human perspective of the ranges of each color. To avoid ambiguity, we consider eleven major colors, namely yellow, blue, green, red, pink, violet, white, black, orange, brown, and grey. We record the center of the element along with the color."
+        ],
+        "bbox": [
+            222,
+            554,
+            787,
+            811
+        ],
+        "page_idx": 10
+    },
+    {
+        "type": "header",
+        "text": "OmniACT",
+        "bbox": [
+            658,
+            114,
+            730,
+            126
+        ],
+        "page_idx": 10
+    },
+    {
+        "type": "page_number",
+        "text": "11",
+        "bbox": [
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+        ],
+        "page_idx": 10
+    },
+    {
+        "type": "page_footnote",
+        "text": "5 https://icomoon.io/",
+        "bbox": [
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+        ],
+        "page_idx": 10
+    },
+    {
+        "type": "text",
+        "text": "Once all the elements of each category are extracted with their coordinates, we then filter these UI elements by prompting GPT-4 [32]. We ensure that the elements selected are suited only for our task, for which we also provide the task description in our prompts along with the list of elements. Full details of the prompt are provided in the appendix section of the paper. As we observe in Figure 3, given an image from the Expedia application, and a task (\"Click on the Black Location icon and enter the destination as Paris\"), the LLM filters out the elements to retain only \"Going To\", \"Location Icon\", and the Black colored elements from the screen. This is passed as input to the LLM or VLM backbone.",
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+        "text": "6Baselines",
+        "text_level": 1,
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+        "text": "To evaluate the performance of existing language model-based agents, we conduct experiments with both language-based and multimodal baselines. The DetACT module takes in image and text descriptions of the task and outputs the color, icon, and text-based signals. This is concatenated to the prompt for the LLM prompt-based baselines (see Figure 4). Every prompt starts with a role assignment [60], followed by the detailed API reference of the PyAutoGUI function set, along with a textual description of their function. We then add five in-context examples from the training set that most closely match the task (based on the cosine similarity of the MiniLM [50] embeddings of the reference task and the train examples). We add a list of UI elements filtered by the DetACT module to the prompt. Finally, we provide the rules with the task description. For multimodal baselines, we also pass the image pixels to the vision encoder. We choose coordinate-based UI elements in the prompt as recent techniques like the Set-of-Mark (SOM) [54] prompting does not work for desktop settings since it is difficult to obtain interactive elements from the desktop screen images. We report the results of several baselines:",
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+            "- Few-shot Generative LLM: We experiment with models from LLaMA-2 [47], Vicuna-1.5 [7], CodeLLaMA-34B [37], Palmyra [46], and GPT [32] series. We use the prompts structure as shown in Figure 4 to prompt the model. For LLaMA and CodeLLaMa, we reduce the prompt length to 2000 tokens by removing outputs from the DetACT module with lower confidence, as we observed poor performance on longer prompts. For the other models, we allow prompts with up to 4000 token sizes.",
+            "- Finetuned Generative LLM: We fine-tuned the LLaMA-13B model and Vicuna-13B using QLoRa [10] with rank 64 and scaling factor 16 for 300 steps to generate the code given screen description from the DetACT module and the instruction.",
+            "- Few-shot Generative Multimodal Models: As OmniACT is predominantly multimodal, with a majority of tasks being visually grounded, we conduct experiments with large multimodal models. Given the limited research in this domain [51, 57], there is a scarcity of available multimodal models with significant size adept for this task. Here, we experiment with [26, 27], providing a similar prompt as well as the screen image."
+        ],
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+        "type": "page_number",
+        "text": "12",
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+    {
+        "type": "header",
+        "text": "R. Kapoor, Y. Butala et al.",
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+    {
+        "type": "text",
+        "text": "7 Results and Analysis",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "As shown in Table 4, we experiment with three different categories of models, namely Prompt-based LLMs, Fine-tuned LLMs, and Prompt-based Multimodal Models. GPT-4 is the best-performing approach, scoring higher on the sequence score and invoking lower penalties on coordinate predicting and text input. For prompt-only LLMs, the GPT-3.5-turbo and GPT-4 models outperform the other LLM baselines, including the LLaMA [47] and Vicuna [7] models. We observe that CodeLLaMA-34B [38], which is trained for code generation, also achieves a higher performance than other models of the same size at predicting the action sequences.",
+        "bbox": [
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+        "type": "text",
+        "text": "Fine-tuned models also perform much better than their few-shot prompt-only counterparts. Fine-tuning substantially improves LLaMA-13B's sequence score (4.80 to 8.92) and action score (1.62 to 2.14), as well as the other metrics. Despite this, we observed that both, prompt-based LLMs and finetuned LLMs face severe mouse penalties, especially on click coordinates. This is because they rely solely on text-based signals.",
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+    {
+        "type": "table",
+        "img_path": "images/0eda75a4d05cbc94fa4e0a4e8b0ed997ce4e639ad39c5f84ffd4f568682a7b24.jpg",
+        "table_caption": [
+            "Table 4: Baseline Performance. (A) Prompt-only LLMs, (B) Fine Tuned LLMs, (C) Prompt-only Multimodal Models. The table represents the Sequence score (SS), click penalty  $(M_p)$ , Key penalty  $(K_p)$ , Write Penalty  $(W_p)$ , and Action Score (AS). The best results for the (SS) and (AS) are highlighted."
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Model</td><td>SS(↑)</td><td>Mp</td><td>Kp</td><td>Wp</td><td>AS(↑)</td></tr><tr><td colspan=\"6\">Prompt based LLMs</td></tr><tr><td>LLaMA-7B [47]</td><td>4.12</td><td>1.24</td><td>1.83</td><td>0.57</td><td>0.48</td></tr><tr><td>Vicuna-7B [7]</td><td>3.88</td><td>1.17</td><td>1.51</td><td>0.43</td><td>0.77</td></tr><tr><td>LLaMA-13B [47]</td><td>4.80</td><td>1.32</td><td>0.93</td><td>0.93</td><td>1.62</td></tr><tr><td>Vicuna-13B [7]</td><td>5.44</td><td>1.65</td><td>0.94</td><td>1.06</td><td>1.78</td></tr><tr><td>Palmyra-Instruct-30B [45]</td><td>7.51</td><td>5.68</td><td>0.12</td><td>0.40</td><td>1.31</td></tr><tr><td>CodeLLaMA-34B [38]</td><td>10.09</td><td>2.99</td><td>2.71</td><td>0.66</td><td>3.72</td></tr><tr><td>Palmyra-X 43B [2]</td><td>11.20</td><td>3.12</td><td>3.02</td><td>2.12</td><td>2.94</td></tr><tr><td>GPT-3.5-turbo-0613 [33]</td><td>22.85</td><td>8.13</td><td>4.51</td><td>2.31</td><td>7.89</td></tr><tr><td>GPT-4 [32]</td><td>32.75</td><td>10.27</td><td>6.99</td><td>3.89</td><td>11.60</td></tr><tr><td colspan=\"6\">Finetuned LLMs</td></tr><tr><td>LLaMA-13B FT</td><td>8.92</td><td>4.61</td><td>1.43</td><td>0.74</td><td>2.14</td></tr><tr><td>Vicuna-13B FT</td><td>8.78</td><td>4.12</td><td>1.31</td><td>0.63</td><td>2.72</td></tr><tr><td colspan=\"6\">Multimodal LLMs</td></tr><tr><td>LLaVA-v1.5-7B [27]</td><td>13.23</td><td>4.73</td><td>1.24</td><td>1.44</td><td>5.82</td></tr><tr><td>LLaVA-v1.5-13B [26]</td><td>20.56</td><td>6.07</td><td>3.44</td><td>2.85</td><td>8.19</td></tr><tr><td>Gemini-Pro [44]</td><td>30.98</td><td>9.05</td><td>6.81</td><td>3.66</td><td>11.46</td></tr><tr><td>GPT-4V [26]</td><td>38.72</td><td>10.53</td><td>7.14</td><td>4.03</td><td>17.02</td></tr><tr><td>Human Performance</td><td>82.23</td><td>0.12</td><td>0.36</td><td>1.61</td><td>80.14</td></tr></table>",
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "To address this, we experiment with multimodal language models (Table 4). We observe that the coordinate prediction improves significantly when we provide the entire image as input to the multimodal LLM, as this enables it to fully utilize the screen representation. In addition to open sourced models, we also experiment with the GPT-4-vision API [55] which shows that GPT-4 Vision [55] outperforms GPT-4 significantly on the Action Score along with improving the sequence score, which we attribute to the strong reasoning abilities of GPT-4 coupled with the improved visual understanding capabilities of the GPT-4-vision model [55]. These findings pave the way towards exciting new research directions on building multimodal models for long-horizon planning and code generation.",
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+    {
+        "type": "text",
+        "text": "Human performance over the task: OmniACT consists of visually complicated tasks, and tests various types of computer skills. In order to get a gauge of how well humans perform, we collect evaluation data from human evaluators. We split the test set uniformly amongst 10 human evaluators, and provided them with the screenshot and task instruction. We record the actions taken by the an-",
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+    {
+        "type": "header",
+        "text": "OmniACT",
+        "bbox": [
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+    },
+    {
+        "type": "page_number",
+        "text": "13",
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+    },
+    {
+        "type": "text",
+        "text": "notators, and measure their performance on our predefined metrics (Table 4). We find that users generally exhibit a high level of proficiency when attempting most tasks for the first time. However, there are instances where users face difficulties in successfully completing certain tasks. These are due to factors including the user's inability to fully comprehend the task, difficulties in grounding the task to the provided screenshot, or a lack of familiarity with the UI.",
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+    {
+        "type": "text",
+        "text": "8 Conclusion and Future Work",
+        "text_level": 1,
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "Autonomous virtual agents offer the potential to automate routine tasks, benefiting users with limited technical expertise. To solve this task, we introduce OmniACT, a unique dataset of 9.8K human-labeled data points. OmniACT benchmarks autonomous agents across a range of tasks on web and desktop applications. LLM-based agents, like GPT-4, achieve a respectable action score of 11.6 on our dataset. However, OmniACT presents a challenge for the current state-of-the-art language and multimodal models. It provides a direction for future research on foundational multimodal models that seamlessly integrate language and visual understanding of computer screens and stands poised to drive the next wave of advancements in generalist autonomous agents offering omnipotent assistance to humans.",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "9 Limitations",
+        "text_level": 1,
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "This work introduces a valuable dataset, yet we recognize a few limitations that exist. State-of-the-art models like GPT-4, may exhibit susceptibility to hallucinations and bias towards specific data types, hindering broad applicability. Reliance on closed models like GPT-4V poses integration challenges due to high costs and time constraints. Despite efforts for equal representation and data collection without personal information, biases may be introduced as the dataset is exclusively in English, and human-curated content may have temporal biases.",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "10 Ethics Statement",
+        "text_level": 1,
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "As a part of the dataset creation process, we carefully review the pipeline at every stage, ensuring there is no personally identifiable information or offensive content, either during data collection or through the use of LLMs. For all purposes, we create dummy accounts that mimic real-like user content. To get the gold labels scripts we seek help from well-qualified student workers, approved through the institution, and get the bounding box data annotated through MTurk workers, both of whom are paid $25 per hour, which is greater than the minimum wage rate (We detail this process in the supplementary material). Human studies are also done with the help of student workers approved by the institution at the above-mentioned payscale. We also ensure that all groups have equitable representation and that no personal opinions are reflected in the dataset, avoiding bias during the collection as well as the annotation process.",
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+    },
+    {
+        "type": "page_number",
+        "text": "14",
+        "bbox": [
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+    },
+    {
+        "type": "header",
+        "text": "R. Kapoor, Y. Butala et al.",
+        "bbox": [
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+        ],
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+    },
+    {
+        "type": "text",
+        "text": "Acknowledgements",
+        "text_level": 1,
+        "bbox": [
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+        ],
+        "page_idx": 14
+    },
+    {
+        "type": "text",
+        "text": "We extend our heartfelt gratitude to Writer.com for their generous and unwavering support throughout this project. Their dedicated team's expertise and collaboration were invaluable in achieving our goals.",
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "References",
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+        "bbox": [
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+        "page_idx": 14
+    },
+    {
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+        ],
+        "bbox": [
+            212,
+            146,
+            784,
+            839
+        ],
+        "page_idx": 16
+    },
+    {
+        "type": "header",
+        "text": "OmniACT",
+        "bbox": [
+            658,
+            114,
+            730,
+            126
+        ],
+        "page_idx": 16
+    },
+    {
+        "type": "page_number",
+        "text": "17",
+        "bbox": [
+            767,
+            114,
+            785,
+            126
+        ],
+        "page_idx": 16
+    },
+    {
+        "type": "list",
+        "sub_type": "ref_text",
+        "list_items": [
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+    }
+]

2024/OmniACT_ A Dataset and Benchmark for Enabling Multimodal Generalist Autonomous Agents for Desktop and Web/4d3b02b6-e05d-4c83-bcd1-dea853d30a58_origin.pdf

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2024/OmniACT_ A Dataset and Benchmark for Enabling Multimodal Generalist Autonomous Agents for Desktop and Web/full.md

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+# OmniACT: A Dataset and Benchmark for Enabling Multimodal Generalist Autonomous Agents for Desktop and Web
+
+Raghav Kapoor<sup>1*</sup>, Yash Parag Butala<sup>1*</sup>, Melisa Russak<sup>2</sup>, Jing Yu Koh<sup>1</sup>, Kiran Kamble<sup>2</sup>, Waseem AlShikh<sup>2</sup>, and Ruslan Salakhutdinov<sup>1</sup>
+
+1 Carnegie Mellon University
+
+$^{2}$  Writer.com
+
+{raghavka,ypb}@cs.cmu.edu
+
+https://huggingface.co/datasets/Writer/omniact
+
+![](images/fb400ca699f9d9bd89b67da7f139d9ad5d2f0a68d5306cf940f4420398338896.jpg)  
+Fig. 1: OmniACT dataset and benchmark for enabling autonomous human-computer interaction agents. The left shows an image paired with a natural language task description as the input, the right shows the resulting action script to be executed on the screen. Examples are presented from Stocks, Apartments, and Weather application.
+
+# Task Description
+
+Check the rate of change in Google's stock price over last one month
+
+Find Rental Properties in Seattle, Washington
+
+Using the popup opened, scroll over to find weather in Chicago on 18th September
+
+# Action Script
+
+import pyautogui
+
+pyautogui.moveTo(210, 179)  
+pyautoguidragTo(243, 179, button="left")
+
+import pyautogui
+
+pyautogui.click(398,167) pyautogui.write("Seattle,WA") pyautogui.write("enter")
+
+import pyautogui
+
+pyautogui.goiveFo(410, 207)  
+pyautogui.hscroll(10)  
+pyautogui.clclick(410, 207)
+
+# Illustration
+
+![](images/fbff3ff17c5112751dfce0ff6d8f3cb2e02d617fb6b14f2b8f659187ae31e250.jpg)
+
+![](images/3a67c4c9abf4c04345eae133c5acfb623d982b7ab80e57194b856869ab0cff89.jpg)
+
+![](images/61ca544866e92d7d2bf7f122d060d7b52459617b33e9c37ec3b129c83b8a31c6.jpg)
+
+![](images/6e3c7cb3e88bffc5744b780c3183e94f4fc7ca0c664132376252b5207140f516.jpg)
+
+Abstract. For decades, human-computer interaction has fundamentally been manual. Even today, almost all productive work done on the computer necessitates human input at every step. Autonomous virtual agents represent an exciting step in automating many of these menial tasks. Virtual agents would empower users with limited technical proficiency to harness the full possibilities of computer systems. They could also enable the efficient streamlining of numerous computer tasks, ranging from calendar management to complex travel bookings, with minimal human intervention. In this paper, we introduce OmniACT, the first-of-a-kind
+
+dataset and benchmark for assessing an agent's capability to generate executable programs to accomplish computer tasks. Our scope extends beyond traditional web automation, covering a diverse range of desktop applications. The dataset consists of fundamental tasks such as "Play the next song", as well as longer horizon tasks such as "Send an email to John Doe mentioning the time and place to meet". Specifically, given a pair of screen image and a visually-grounded natural language task, the goal is to generate a script capable of fully executing the task. We run several strong baseline language model agents on our benchmark. The strongest baseline, GPT-4, performs the best on our benchmark. However, its performance level still reaches only  $15\%$  of the human proficiency in generating executable scripts capable of completing the task, demonstrating the challenge of our task for conventional web agents. Our benchmark provides a platform to measure and evaluate the progress of language model agents in automating computer tasks and motivates future work towards building multimodal models that bridge large language models and the visual grounding of computer screens.
+
+Keywords: Generalist Agent  $\cdot$  Multimodal Machine Learning  $\cdot$  Dataset and Benchmark  $\cdot$  Vision Language Understanding  $\cdot$  UI grounding  $\cdot$  Human-computer interaction
+
+# 1 Introduction
+
+Performing computer tasks based on natural language instructions has been a long-standing goal of artificial intelligence [49]. One concrete objective in the line of research is to develop generalist agents that can assist humans in doing computer tasks [21], such as "Order a pizza from Domino's" or "Write a message to John." The agent should be able to open the application and perform the task. Executing these actions on a personal computer involves a sequence of interactions with a mouse and keyboard. For example, the simple task of writing an email involves hovering over the application icon, clicking it, clicking the 'New Email' button, writing the content of the email, and clicking send. Successfully sending an email requires accurately predicting the correct action at each step and accurately executing it, which is a herculean task even for the best agents today [14].
+
+A generalist agent for computer tasks must understand natural language instructions, process visual screenshots, and produce the correct sequence of actions to be performed to achieve the intended task. Several existing approaches focus on building agents based on the HTML model [9, 40, 62]. However, this approach introduces several challenges and constraints. These agents are limited to web applications and often struggle with complex or long-context HTML code. They cannot interact with native desktop applications or perform tasks that span multiple applications, like drafting an email using text from a code editor, without significant alterations. Furthermore, HTML-based agents, which are inherently powered by text-only language models, typically underperform in tasks requiring visual cues, such as identifying and clicking a blue button
+
+on a desktop's top-right corner. In contrast, humans can easily understand UI elements like dropdown menus, typable areas, redirections, and options with just a glance.
+
+Towards the goal of developing a generalist autonomous agent with robust visual and user interface (UI) understanding capabilities, we introduce a new task and dataset, OmniACT, containing over 9.8K pairs of images and instructions (Figure 1) across different operating systems and the web. This dataset includes screenshots of various UI screens and corresponding natural language instructions. The objective of these instructions is to generate executable commands using the PyAutoGUI Python library [1]. PyAutoGUI enables the automation of the mouse and keyboard operations, which helps to facilitate interactions with various native applications across macOS, Windows, and Linux. This simplifies completing specified tasks across different web domains and native desktop applications.
+
+We evaluate several language model-based agent baselines on this dataset, including LLaMA [47], Vicuna [7], Palmyra-X (43B) [2], InstructPalmyra-30B [45], GPT 3.5, and GPT-4 [32]. We experiment with fine-tuning Vicuna-13B and LLaMA-13B models using QLoRA [10]. We also benchmark multimodal baseline LLaVa-v1.5-7B, LLaVa-v1.5-13B [47], Gemini-Pro [44] and GPT-4-vision-preview [55] for the task. Our findings highlight the necessity for a multimodal model capable of executing these tasks, and our analysis provides insights into promising future work in the space. Our key contributions are outlined as follows:
+
+1. We release a novel dataset of desktop and website applications consisting of over 9.8K natural language tasks, UI screens, and corresponding code snippets collected through human annotation. We introduce custom performance metrics tailored for computer tasks.  
+2. We propose DetACT, a module for creating textual representations of the screen using signals from OCR, color, and icon-template matching.  
+3. We conduct a comprehensive benchmark and analysis of state-of-the-art LLMs and multimodal models on our benchmark. Our results show that OmniACT is a challenging task for even the best LLM agents today, and existing models are far below human performance.
+
+# 2 Related Work
+
+# 2.1 UI Understanding
+
+User interface (UI) understanding has garnered interest from researchers in the machine learning and human-computer interaction communities, evolving with various models focusing on understanding the semantics of mobile and web user interfaces. UIBert [3], PixelBERT [16], ActionBert [15], VUT [25], Screen2Words [48], WidgetCaptioning [24] and Pix2Act [39] are notable models in this area. They propose approaches for learning the user-interface semantics of the mobile screen using the image and view hierarchy. These models have demonstrated effectiveness in tasks like capability prediction, screen segmentation and
+
+Table 1: Comparison of OmniACT with other related benchmarks.  
+
+<table><tr><td>Datasets</td><td>Size</td><td>Env Type</td><td>Task Heterogeneity</td><td>Real-World Portrayal</td><td>Executional Correctness</td><td>Supports Continuous Scale Desktop Apps</td><td>Adaptive Evaluation</td><td>Task</td></tr><tr><td>VisualWebArena [20]</td><td>910</td><td>Web</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>Web Navigation</td></tr><tr><td>WebArena [62]</td><td>812</td><td>Web</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>Web Navigation</td></tr><tr><td>Mind2Web [9]</td><td>2350</td><td>Web</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td>Web Navigation</td></tr><tr><td>WebShop [56]</td><td>12000 Products</td><td>Web</td><td>No</td><td>No</td><td>Yes</td><td>No</td><td>No</td><td>Web Navigation</td></tr><tr><td>RUSS [53]</td><td>80</td><td>Web</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td>Web Navigation</td></tr><tr><td>WebSRC [6]</td><td>2735</td><td>Web</td><td>Yes</td><td>Yes</td><td>-</td><td>No</td><td>No</td><td>QA</td></tr><tr><td>MiniWoB++ [17]</td><td>100</td><td>Mobile Websites</td><td>No</td><td>No</td><td>Yes</td><td>No</td><td>No</td><td>Web Navigation</td></tr><tr><td>PixelHelp [23]</td><td>187</td><td>Mobile</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td>UI Grounding</td></tr><tr><td>MetaGUI [42]</td><td>1125</td><td>Mobile</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>Mobile Navigation</td></tr><tr><td>MoTIF [5]</td><td>756</td><td>Mobile</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>Mobile Navigation</td></tr><tr><td>AITW [35]</td><td>715142</td><td>Mobile and Web</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>Mobile/Web Navigation</td></tr><tr><td>OmniACT (Ours)</td><td>9802</td><td>Desktop and Web</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Code Generation</td></tr></table>
+
+understanding, and screen caption generation. Lexi [4] and Spotlight [22] propose models that use vision-only inputs to minimize the reliance on metadata such as view hierarchy. Furata et al. [11] demonstrates the use of fine-tuning for multimodal web navigation. The majority of machine learning models trained for UI understanding leverage the Rico dataset [8] and its extensions, which contain 64,462 unique Android screens and metadata. In addition, [4] released the UICaptions dataset, which consists of diverse image-captions pairs across a wide range of applications. PixelHelp [23] also released a corpus to train models that can interpret natural language instructions and map them to mobile UI actions.
+
+# 2.2 Autonomous Computer Agents
+
+The advent of large language models (LLMs) has been pivotal in the rapid advancement of agents that operate on web pages. Recent research such as ViperGPT [43] Chameleon [29], RCI Agent [18], VisProg [12], and [31] employ LLMs for planning or action prediction in developing autonomous agents. Benchmark datasets, such as MiniWoB [40], WebShop [56], Macaw-LLM [30], ASH-Prompting [41] Mind2Web [9], WebArena [62], AgentBench [28] and VisualWebArena [20] have also been proposed to measure the ability of LLM-based agents to automate web tasks. These methods mainly involve agents that operate on a text-based Document Object Model (DOM) of HTML scripts. This limits their understanding of screen context, which is crucial for the model's decision-making and action-taking processes. To address this limitation, [35] released Android in the Wild, a dataset comprising screens, natural language instructions, and corresponding actions. Following this, [59] proposed a multimodal model, AutoUI, which is designed to build an agent on the Android in the Wild dataset confined to the Android ecosystem. WebAgent [13] utilized Flan-U-PaLM, for grounded code generation, and HTML-T5 and showed improvement on real-world websites.
+
+Current benchmarks for autonomous agents focus mainly on the Web or Android environments, posing challenges for tasks involving desktop applications or spanning multiple applications beyond the web domain. The absence of established benchmarks and datasets in this area, coupled with basic methods
+
+for extracting user interface (UI) elements, underscores the need for significant progress in developing more versatile autonomous agents capable of handling diverse tasks beyond the current scope. To highlight the unique features that OmniACT introduces in the assessment of capable autonomous agents, we provide a comparison between the existing benchmarks and our proposed benchmark, OmniACT, in Table 1.
+
+![](images/3637b64b82edc539bc8230362a244c2b1b77bf9d9c6277baaa4ab34339e3319e.jpg)  
+Fig. 2: Data Collection Pipeline. (1) We select over 60 applications and websites to ensure diversity, (2) segment the screen through human-annotated bounding boxes, (3) label the bounding boxes based on functionality, (4) ask student volunteers to come up with tasks, given a screen image, and (5) reverse map the textual labels to coordinates and filter the scripts based on execution and syntax.
+
+# 3 OmniACT
+
+We introduce a novel dataset and benchmark, OmniACT, which measures the performance of autonomous agents on both web and desktop applications. Compared to previous benchmarks which focus on text-based reasoning [9, 17, 40, 56, 62], our benchmark aims to measure multimodal agents that bridge large language model planners and UI understanding vision models. OmniACT can be accomplished as a standalone task as it is not under a mock environment.
+
+All actions that a human can execute on the computer can be encoded in the PyAutoGUI [1] Python framework. This framework allows a user to execute keyboard and mouse operations by running Python code. The PyAutoGUI code to execute these tasks is shown in the third column of Figure 1. For other computer tasks, the PyAutoGUI library provides functions such as 'press', 'write', and 'scroll' which can be used to execute the task. Our dataset consists of parallel data of natural language tasks, UI screenshots, and ground truth PyAutoGUI scripts that achieve successful execution.
+
+# 3.1 Task Formulation
+
+Given an input state of a computer defined by the screen  $S$  and the task description  $T$  in natural language, the goal of the task is to output a sequence of actions  $A$  that can successfully accomplish the task  $T$  within a screenshot  $S \in \{ \text{Linux, Windows, MacOS, Webpage} \}$ . Formally, the task can be defined as learning the transition function  $f: T \times S \to A$ . During dataset collection, we ensure that all task descriptions  $T$  are feasible and can be accomplished in the current screenshot  $S$ . To reduce ambiguity and facilitate better evaluation, we ensure that task descriptions are detailed and unambiguous. Tasks can also be visually grounded (e.g., 'Click the red button to start recording') or natural language based (e.g., 'Click the My Account button'). We define the action space using the functionalities in the PyAutoGUI library:  $A \in \{\text{'click', 'dragTo', 'scroll', 'write', \ldots}\}$ . The exhaustive list of actions is provided in Table 2. Our action space is much larger than other benchmarks [9,40,62] that resort to two or three interaction options. Mouse actions such as 'moveTo', 'click', 'rightClick', 'doubleClick', and 'dragTo', additionally require screen coordinates as arguments, which indicate the pixel location of the action.
+
+Figure 1 illustrates sample tasks and corresponding outputs for three applications within OmniACT: (1) Stocks (MacOS), (2) Apartments.com (web page), and (3) Weather (MacOS). The first column depicts the input image, and the second column shows the natural language task that is to be executed on the current screen. To execute these tasks, a user must accurately perform a series of operations using the mouse and keyboard. Eg: to check the rate of change in Google's stock price over the last month, the mouse has to be moved to the last month and dragged while holding the left-click button to the current month.
+
+# 3.2 Dataset Preparation
+
+To prepare our dataset, we followed a pipelined approach, as summarized in Figure 2. We first selected a variety of applications and websites. For each application or website, we created bounding boxes around key UI elements and labeled them according to their functionality, which is crucial for assisting human annotators in writing accurate PyAutoGUI scripts. After each script is written, we converted the labels back into numeric coordinates, allowing us to align the scripts precisely with the locations of the UI elements. Finally, we thoroughly reviewed each script, focusing on its executability and adherence to syntax standards. This ensured the high quality and functionality of our dataset, making it a valuable resource for training and evaluating autonomous agents.
+
+Application/Website Selection To test the computer agents' generalization ability across different tasks, we collect tasks across multiple domains on both desktop and web applications. In total, we collect and annotate 9802 data points (Table 3), with the split between desktop and web applications approximately 3:1. The emphasis on desktop applications, which do not contain Document Object Model (DOM) hierarchies unlike HTML-based web pages, presents a more
+
+complex multimodal challenge where visual cues are crucial. We collect tasks from applications within the three most popular operating systems. We select 22 native applications from MacOS, and 8 each from Linux and Windows. We annotate roughly 3 to 4 screens for every application. The full list of applications is provided in the Appendix.
+
+Many common computer tasks today are still performed through web applications, so we also collect 3-4 screenshots from 27 different web applications. To ensure diversity in task intents, we categorize these tasks into one of the following 6 categories: (1) Shopping, (2) Entertainment, (3) Service, (4) Government, (5) Travel, (6) Health. Inspired by the methodology of [9], these categories were selected to cover a wide range of user intents and functionalities.
+
+Table 2: Action types supported by OmniACT and the number of instances for each action in the dataset.  
+
+<table><tr><td>Type</td><td>Action</td><td>%</td></tr><tr><td rowspan="7">Mouse</td><td>Click</td><td>63.73</td></tr><tr><td>Double Click</td><td>0.58</td></tr><tr><td>Right Click</td><td>0.77</td></tr><tr><td>Move/Hover</td><td>1.85</td></tr><tr><td>Drag</td><td>0.29</td></tr><tr><td>Scroll</td><td>1.68</td></tr><tr><td>Horizontal Scroll</td><td>0.17</td></tr><tr><td rowspan="3">Keyboard</td><td>Press</td><td>16.28</td></tr><tr><td>Hotkey</td><td>3.00</td></tr><tr><td>Write</td><td>11.65</td></tr></table>
+
+UI Screen Segmentation To collect gold-standard data, we first annotate and segment the screen by identifying the
+
+bounding boxes present on the screen. We employ slightly different techniques for web and desktop applications to create the bounding boxes:
+
+1. Desktop Applications: We build a custom annotation interface based on PyQt5 $^3$  to create bounding boxes manually over a screen image using a simple drag-and-click mechanism. This custom interface expedites the process and allows us to get highly accurate gold-label data for desktop images.  
+2. Websites: For webpages, we write JavaScript code to extract all interactable (click, type, etc.) regions from HTML source code. We also extract banners, dropowns, submit, and radio buttons from the screen. We filter the elements to retain only those that are visible and interactable within the screen.
+
+Functionality Tagging To map each bounding box to its correct functional description, we leverage Amazon MTurk workers (see details in Appendix), who are given an image with a bounding box and are required to write the correct description or label of the bounding box's function. For example, given an image of an Amazon webpage with a search bar, the annotator labels it as "find-product-search-bar". The logical descriptions are used to create tasks in a structured manner without the need to identify individual bounding box coordinates.
+
+Task Creation Our approach for each screen involves utilizing all human-annotated bounding boxes and their labels to create tasks that can be executed
+
+![](images/91bc6bf190024aeb93b022e0af17e277beeacd4e3c1c9b2f438622bc2f6c41f0.jpg)  
+Fig. 3: DetACT Module. Given an initial image and a natural language task description, we use a pipelined approach to run OCR and SAM on the screen. The outputs from SAM are then used by icon and color-matching modules to obtain an exhaustive set of useful UI elements. The list of elements is passed through LLM based filter to select only the elements related to the given task.
+
+within the confines of a single screen. These tasks are designed to be visually grounded in order to measure the capabilities of multimodal agents. We plan to release the bounding box and their corresponding labels as the metadata for evaluation purposes.
+
+For dataset compilation, college students with basic Python programming skills served as annotators, accessing API references for PyAutoGUI and examples of potential tasks. Each student generated multiple tasks, each accompanied by three alternative natural language reformulations. For instance, "What is  $3 + 2$ ?" might be reformulated as "Calculate the sum of 2 and  $3$ " or "Add two to three". To avoid train-test leakage, rephrased tasks were consistently placed in the same dataset split. Further details on the annotation process are available in the Appendix.
+
+Reverse Mapping and Filtering To ensure high-quality data, we incorporate an additional step into the data collection pipeline. We build scripts to map the text-based labels of each bounding box back to their numeric coordinates, and then match the syntax and verify if the task will be executed on the screen. Using this filter, we remove all the non-working or syntactically incorrect data points and finally manually review the set of tasks.
+
+After filtering, we obtain 9802 human-annotated, gold-label data points across more than 200 desktop and web screens
+
+Table 3: Dataset distribution across splits and platforms.  
+
+<table><tr><td colspan="2">Domain</td><td>Train Validation</td><td>Test</td><td>Total</td></tr><tr><td rowspan="3">Desktop</td><td>Mac OS</td><td>3028</td><td>444</td><td>786</td></tr><tr><td>Linux</td><td>761</td><td>126</td><td>247</td></tr><tr><td>Windows</td><td>1573</td><td>216</td><td>458</td></tr><tr><td>Web</td><td>-</td><td>1427</td><td>206</td><td>530</td></tr><tr><td>Total</td><td></td><td>6789</td><td>992</td><td>2,021</td></tr></table>
+
+(Table 3), split into train, validation, and test sets in a 7:1:2 ratio. All collected data will be publicly released to encourage future work on multimodal agents.
+
+# 4 Evaluation Metrics
+
+In this section, we detail various evaluation metrics for benchmarking model performance on the OmniACT dataset. UI screens have additional constraints such as spatial relevance which are not factored in most conventional similarity-based metrics such as BLEU [34], CodeBLEU [36], BERTScore [58] and CodeBERTScore [61]. For example, a valid click action is usually not constrained to a single coordinate but can be any coordinate within a specified region. In the event of invalid coordinate predictions, an agent that predicts coordinates further away from the valid region should invoke a higher penalty compared to an agent that predicted coordinates close to the region. We propose two new metrics adapted: Sequence Score (Section 4.1) and Action Score (Section 4.2) aimed at utilizing UI information.
+
+# 4.1 Sequence Score
+
+The sequence score measures whether the predicted action sequence (e.g., 'click', 'write', 'press') exactly matches the gold sequence. Since predicting the first action in the sequence is relatively straightforward and later actions are more difficult, we define sequence score as follows:
+
+$$
+\operatorname {S e q S c o r e} _ {i} = \left\{ \begin{array}{l l} \beta_ {1} + \beta_ {2} * (s - 1) & \text {i f a l l a c t i o n s m a t c h} \\ 0 & \text {o t h e r w i s e} \end{array} \right. \tag {1}
+$$
+
+where  $s$  is the action sequence length,  $\beta_{1}$  is set to 0.1 and  $\beta_{2}$  is set to 1.
+
+# 4.2 Action Score
+
+The action score measures how well a code snippet containing the correct action sequence can perform the task. Specifically, for a script with a correct action sequence, we introduce penalties for inaccurate behavior. The penalties are described below:
+
+1. Click penalty  $(M)$ : For actions 'click', 'rightClick', 'doubleClick', 'moveTo', and 'dragTo', we penalize code snippets where predicted coordinates lie outside of the bounding box of the UI element. The click penalty for the  $j^{th}$  action of the  $i^{th}$  example is defined as:
+
+$$
+M _ {i} ^ {j} = \alpha_ {i} \times \left\{ \begin{array}{l l} 1 - \frac {\mu}{\mu + L _ {2}} & \text {i f S e q S c o r e} _ {i} > 0 \\ 1 & \text {o t h e r w i s e} \end{array} \right. \tag {2}
+$$
+
+Here  $L_{2}$  corresponds to the smallest Euclidean distance between the predicted coordinate and bounding box.  $L_{2}$  is zero when the predicted coordinate lies within the target bounding box.  $\mu$  is the Dirichlet smoothing
+
+coefficient which we dynamically set to the inverse of the length of the diagonal of the bounding box. This ensures that the penalty for points outside the bounding box varies based on the size of the bounding box. For two predicted points with the same  $L_{2}$ , the metric penalizes more heavily if the box is larger. This is sound with the intuition that the chances of clicking on a larger box are higher and should be penalized more in case of a mistake.
+
+2. Key penalty  $(K)$ : For actions 'press' and 'hotkey', we check whether the set of keys in the target code (represented as  $GK_{i}^{j}$ ) and predicted code (represented as  $PK_{i}^{j}$ ) are the same. It is formally defined as:
+
+$$
+K _ {i} ^ {j} = \alpha_ {i} \times \left\{ \begin{array}{l l} 0 & \text {i f} G K _ {i} ^ {j} = P K _ {i} ^ {j} \text {a n d S e q S c o r e} _ {i} > 0 \\ 1 & \text {o t h e r w i s e} \end{array} \right. \tag {3}
+$$
+
+3. Write penalty  $(W_{p})$ : For action type 'write', we penalize the output for the sentence to be typed. Specifically, we the employ BLEU score [34], and compute:
+
+$$
+W _ {i} ^ {j} = \alpha_ {i} \times \left\{ \begin{array}{l l} 1 - B L E U \left(G S _ {i} ^ {j}, P S _ {i} ^ {j}\right) & \text {i f S e q S c o r e} _ {i} > 1 \\ 1 & \text {o t h e r w i s e} \end{array} \right. \tag {4}
+$$
+
+Here,  $GS_{i}^{j}$  represents the actual sentence to be typed, and  $PS_{i}^{j}$  represents the sentence predicted by the model in the  $j^{th}$  action of example  $i$ .
+
+In the above equations,  $(\alpha_{i})$  is the weighting factor:
+
+$$
+\alpha_ {i} = \operatorname {S e q S c o r e} _ {i} / \text {l e n g t h o f s e q u e n c e} i \tag {5}
+$$
+
+This ensures that the action score  $\in [0,1]$ . The mean action score is calculated as follows:
+
+$$
+\text {A c t i o n S c o r e} = \frac {\sum_ {i} \max  \left(\operatorname {S e q S c o r e} _ {i} - \sum_ {j} \left(M _ {i} ^ {j} + K _ {i} ^ {j} + W _ {i} ^ {j}\right) , 0\right)}{\sum_ {i} \operatorname {S e q S c o r e} _ {i}} \tag {6}
+$$
+
+# 5 DetACT: DETecting ACTions from UI
+
+Understanding UI screens is crucial for multimodal computer tasks. Web-based agents typically use language-only inputs from the HTML DOM. This is insufficient for comprehending the full extent of an application UI, as many components may not be easily described with HTML code. To address this, we propose DetACT, which allows us to convert images of UI layouts into structured code and text outputs for a downstream LLM. DetACT is a system comprised of three distinct modules: the text module, the icon module, and the color module.
+
+1. Text Extraction: We use the EasyOCR model<sup>4</sup> to parse over the UI screens and collect all text-based elements. Along with the text, we also note the locations of each of these elements. This is depicted in Figure 3, along with a list of text elements found on the screen using the OCR Module. We segment and classify the different regions within the screenshot using the
+
+![](images/877231d43634aee2879a1c206ac0fe7070408c08015ff5c3ce1a8c4b850a6958.jpg)  
+Fig. 4: Baseline Model Architecture. Image and task descriptions are sent to DetACT module, which gives a filtered list of UI elements relevant to feed into the prompt along with the task. We also show the prompt structure used for action script generation. This structure is passed through the LLM (along with the image for multimodal LLM) to generate the automation script.
+
+Segment Anything Model (SAM) [19]. From the outputs, we filter out the non-textual segments for our icon and color detection.
+
+2. Icon Module: For matching with the appropriate icon, we use a pack of 1600 icons<sup>5</sup> as templates. Each of these icons is labeled with their appropriate functionality and is matched with the filtered outputs SAM [19]. For the similarity of the two images, we resize the reference icons and segmented region of interest (ROI) to the same size, and convert both images to grayscale. After this, we use the Structural Similarity Index (SSIM) [52], to find the closest match of the ROI to the icons in our set, and select the ones above the SSIM threshold of 0.95. As seen in Figure 3, a few icons matched on the screen are Globe icon, Calendar icon, Person icon, and Location icon; each depicting a different use case.  
+3. Color Module: Finally, to place all segments of interest into appropriate buckets of colors, we average the RGB pixel values over the ROI and, based on that value, bucket them into different color categories. We categorize colors differently based on the human perspective of the ranges of each color. To avoid ambiguity, we consider eleven major colors, namely yellow, blue, green, red, pink, violet, white, black, orange, brown, and grey. We record the center of the element along with the color.
+
+Once all the elements of each category are extracted with their coordinates, we then filter these UI elements by prompting GPT-4 [32]. We ensure that the elements selected are suited only for our task, for which we also provide the task description in our prompts along with the list of elements. Full details of the prompt are provided in the appendix section of the paper. As we observe in Figure 3, given an image from the Expedia application, and a task ("Click on the Black Location icon and enter the destination as Paris"), the LLM filters out the elements to retain only "Going To", "Location Icon", and the Black colored elements from the screen. This is passed as input to the LLM or VLM backbone.
+
+# 6Baselines
+
+To evaluate the performance of existing language model-based agents, we conduct experiments with both language-based and multimodal baselines. The DetACT module takes in image and text descriptions of the task and outputs the color, icon, and text-based signals. This is concatenated to the prompt for the LLM prompt-based baselines (see Figure 4). Every prompt starts with a role assignment [60], followed by the detailed API reference of the PyAutoGUI function set, along with a textual description of their function. We then add five in-context examples from the training set that most closely match the task (based on the cosine similarity of the MiniLM [50] embeddings of the reference task and the train examples). We add a list of UI elements filtered by the DetACT module to the prompt. Finally, we provide the rules with the task description. For multimodal baselines, we also pass the image pixels to the vision encoder. We choose coordinate-based UI elements in the prompt as recent techniques like the Set-of-Mark (SOM) [54] prompting does not work for desktop settings since it is difficult to obtain interactive elements from the desktop screen images. We report the results of several baselines:
+
+- Few-shot Generative LLM: We experiment with models from LLaMA-2 [47], Vicuna-1.5 [7], CodeLLaMA-34B [37], Palmyra [46], and GPT [32] series. We use the prompts structure as shown in Figure 4 to prompt the model. For LLaMA and CodeLLaMa, we reduce the prompt length to 2000 tokens by removing outputs from the DetACT module with lower confidence, as we observed poor performance on longer prompts. For the other models, we allow prompts with up to 4000 token sizes.  
+- Finetuned Generative LLM: We fine-tuned the LLaMA-13B model and Vicuna-13B using QLoRa [10] with rank 64 and scaling factor 16 for 300 steps to generate the code given screen description from the DetACT module and the instruction.  
+- Few-shot Generative Multimodal Models: As OmniACT is predominantly multimodal, with a majority of tasks being visually grounded, we conduct experiments with large multimodal models. Given the limited research in this domain [51, 57], there is a scarcity of available multimodal models with significant size adept for this task. Here, we experiment with [26, 27], providing a similar prompt as well as the screen image.
+
+# 7 Results and Analysis
+
+As shown in Table 4, we experiment with three different categories of models, namely Prompt-based LLMs, Fine-tuned LLMs, and Prompt-based Multimodal Models. GPT-4 is the best-performing approach, scoring higher on the sequence score and invoking lower penalties on coordinate predicting and text input. For prompt-only LLMs, the GPT-3.5-turbo and GPT-4 models outperform the other LLM baselines, including the LLaMA [47] and Vicuna [7] models. We observe that CodeLLaMA-34B [38], which is trained for code generation, also achieves a higher performance than other models of the same size at predicting the action sequences.
+
+Fine-tuned models also perform much better than their few-shot prompt-only counterparts. Fine-tuning substantially improves LLaMA-13B's sequence score (4.80 to 8.92) and action score (1.62 to 2.14), as well as the other metrics. Despite this, we observed that both, prompt-based LLMs and finetuned LLMs face severe mouse penalties, especially on click coordinates. This is because they rely solely on text-based signals.
+
+Table 4: Baseline Performance. (A) Prompt-only LLMs, (B) Fine Tuned LLMs, (C) Prompt-only Multimodal Models. The table represents the Sequence score (SS), click penalty  $(M_p)$ , Key penalty  $(K_p)$ , Write Penalty  $(W_p)$ , and Action Score (AS). The best results for the (SS) and (AS) are highlighted.  
+
+<table><tr><td>Model</td><td>SS(↑)</td><td>Mp</td><td>Kp</td><td>Wp</td><td>AS(↑)</td></tr><tr><td colspan="6">Prompt based LLMs</td></tr><tr><td>LLaMA-7B [47]</td><td>4.12</td><td>1.24</td><td>1.83</td><td>0.57</td><td>0.48</td></tr><tr><td>Vicuna-7B [7]</td><td>3.88</td><td>1.17</td><td>1.51</td><td>0.43</td><td>0.77</td></tr><tr><td>LLaMA-13B [47]</td><td>4.80</td><td>1.32</td><td>0.93</td><td>0.93</td><td>1.62</td></tr><tr><td>Vicuna-13B [7]</td><td>5.44</td><td>1.65</td><td>0.94</td><td>1.06</td><td>1.78</td></tr><tr><td>Palmyra-Instruct-30B [45]</td><td>7.51</td><td>5.68</td><td>0.12</td><td>0.40</td><td>1.31</td></tr><tr><td>CodeLLaMA-34B [38]</td><td>10.09</td><td>2.99</td><td>2.71</td><td>0.66</td><td>3.72</td></tr><tr><td>Palmyra-X 43B [2]</td><td>11.20</td><td>3.12</td><td>3.02</td><td>2.12</td><td>2.94</td></tr><tr><td>GPT-3.5-turbo-0613 [33]</td><td>22.85</td><td>8.13</td><td>4.51</td><td>2.31</td><td>7.89</td></tr><tr><td>GPT-4 [32]</td><td>32.75</td><td>10.27</td><td>6.99</td><td>3.89</td><td>11.60</td></tr><tr><td colspan="6">Finetuned LLMs</td></tr><tr><td>LLaMA-13B FT</td><td>8.92</td><td>4.61</td><td>1.43</td><td>0.74</td><td>2.14</td></tr><tr><td>Vicuna-13B FT</td><td>8.78</td><td>4.12</td><td>1.31</td><td>0.63</td><td>2.72</td></tr><tr><td colspan="6">Multimodal LLMs</td></tr><tr><td>LLaVA-v1.5-7B [27]</td><td>13.23</td><td>4.73</td><td>1.24</td><td>1.44</td><td>5.82</td></tr><tr><td>LLaVA-v1.5-13B [26]</td><td>20.56</td><td>6.07</td><td>3.44</td><td>2.85</td><td>8.19</td></tr><tr><td>Gemini-Pro [44]</td><td>30.98</td><td>9.05</td><td>6.81</td><td>3.66</td><td>11.46</td></tr><tr><td>GPT-4V [26]</td><td>38.72</td><td>10.53</td><td>7.14</td><td>4.03</td><td>17.02</td></tr><tr><td>Human Performance</td><td>82.23</td><td>0.12</td><td>0.36</td><td>1.61</td><td>80.14</td></tr></table>
+
+To address this, we experiment with multimodal language models (Table 4). We observe that the coordinate prediction improves significantly when we provide the entire image as input to the multimodal LLM, as this enables it to fully utilize the screen representation. In addition to open sourced models, we also experiment with the GPT-4-vision API [55] which shows that GPT-4 Vision [55] outperforms GPT-4 significantly on the Action Score along with improving the sequence score, which we attribute to the strong reasoning abilities of GPT-4 coupled with the improved visual understanding capabilities of the GPT-4-vision model [55]. These findings pave the way towards exciting new research directions on building multimodal models for long-horizon planning and code generation.
+
+Human performance over the task: OmniACT consists of visually complicated tasks, and tests various types of computer skills. In order to get a gauge of how well humans perform, we collect evaluation data from human evaluators. We split the test set uniformly amongst 10 human evaluators, and provided them with the screenshot and task instruction. We record the actions taken by the an-
+
+notators, and measure their performance on our predefined metrics (Table 4). We find that users generally exhibit a high level of proficiency when attempting most tasks for the first time. However, there are instances where users face difficulties in successfully completing certain tasks. These are due to factors including the user's inability to fully comprehend the task, difficulties in grounding the task to the provided screenshot, or a lack of familiarity with the UI.
+
+# 8 Conclusion and Future Work
+
+Autonomous virtual agents offer the potential to automate routine tasks, benefiting users with limited technical expertise. To solve this task, we introduce OmniACT, a unique dataset of 9.8K human-labeled data points. OmniACT benchmarks autonomous agents across a range of tasks on web and desktop applications. LLM-based agents, like GPT-4, achieve a respectable action score of 11.6 on our dataset. However, OmniACT presents a challenge for the current state-of-the-art language and multimodal models. It provides a direction for future research on foundational multimodal models that seamlessly integrate language and visual understanding of computer screens and stands poised to drive the next wave of advancements in generalist autonomous agents offering omnipotent assistance to humans.
+
+# 9 Limitations
+
+This work introduces a valuable dataset, yet we recognize a few limitations that exist. State-of-the-art models like GPT-4, may exhibit susceptibility to hallucinations and bias towards specific data types, hindering broad applicability. Reliance on closed models like GPT-4V poses integration challenges due to high costs and time constraints. Despite efforts for equal representation and data collection without personal information, biases may be introduced as the dataset is exclusively in English, and human-curated content may have temporal biases.
+
+# 10 Ethics Statement
+
+As a part of the dataset creation process, we carefully review the pipeline at every stage, ensuring there is no personally identifiable information or offensive content, either during data collection or through the use of LLMs. For all purposes, we create dummy accounts that mimic real-like user content. To get the gold labels scripts we seek help from well-qualified student workers, approved through the institution, and get the bounding box data annotated through MTurk workers, both of whom are paid $25 per hour, which is greater than the minimum wage rate (We detail this process in the supplementary material). Human studies are also done with the help of student workers approved by the institution at the above-mentioned payscale. We also ensure that all groups have equitable representation and that no personal opinions are reflected in the dataset, avoiding bias during the collection as well as the annotation process.
+
+# Acknowledgements
+
+We extend our heartfelt gratitude to Writer.com for their generous and unwavering support throughout this project. Their dedicated team's expertise and collaboration were invaluable in achieving our goals.
+
+# References
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+        "text": "OmniNOCS: A unified NOCS dataset and model for 3D lifting of 2D objects",
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+        "text": "Akshay Krishnan $^{1,2}$ , Abhijit Kundu $^{1}$ , Kevis-Kokitsi Maninis $^{1}$ , James Hays $^{2}$ , and Matthew Brown $^{1}$",
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+        "text": "1 Google Research†",
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+        "text": "2 Georgia Institute of Technology",
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+            "Fig. 1: We introduce OmniNOCS, a large-scale dataset with Normalized Object Coordinates (NOCS), instance masks, and 3D box annotations for objects across several classes, domains, and cameras. We also propose NOCSformer, a model trained on OmniNOCS that lifts each 2D object bounding box in an image to its corresponding 3D oriented box (pose) and 3D pointcloud (shape)."
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+        "text": "Abstract. We propose OmniNOCS, a large-scale monocular dataset with 3D Normalized Object Coordinate Space (NOCS) maps, object masks, and 3D bounding box annotations for indoor and outdoor scenes. OmniNOCS has 20 times more object classes and 200 times more instances than existing NOCS datasets (NOCS-Real275, Wild6D). We use OmniNOCS to train a novel, transformer-based monocular NOCS prediction model (NOCsformer) that can predict accurate NOCS, instance masks and poses from 2D object detections across diverse classes. It is the first NOCS model that can generalize to a broad range of classes when prompted with 2D boxes. We evaluate our model on the task of 3D oriented bounding box prediction, where it achieves comparable results to state-of-the-art 3D detection methods such as Cube R-CNN. Unlike other 3D detection methods, our model also provides detailed and accurate 3D object shape and segmentation. We propose a novel benchmark for the task of NOCS prediction based on OmniNOCS, which we hope will serve as a useful baseline for future work in this area. Our dataset and code will be at the project website: https://omninocs.github.io.",
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+        "text": "1 Introduction",
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+    },
+    {
+        "type": "text",
+        "text": "Predicting the 6 Degree-of-Freedom (6DoF) pose and shape of objects from images is a crucial problem in 3D scene understanding. Robots need to understand the location,",
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+        "type": "page_footnote",
+        "text": "†Now at Google DeepMind",
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+    {
+        "type": "text",
+        "text": "shape, and orientation of various objects to grasp and interact with them. Self-driving vehicles need to understand the location and heading of vehicles, pedestrians, and other objects on the road. In particular, the 3D orientation of these objects is crucial to predict their behavior. It is also important in AR/VR applications, as it allows users to interact with objects in meaningful ways. Predicting object shape and pose from monocular images is also a prerequisite to initialize 3D panoptic neural scene graphs [27, 39] and to track objects in videos [42]. These applications require approaches that generalize across a wide range of object classes, environments, and camera models.",
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+        "text": "The problem of localizing 3D objects has been extensively studied through the lens of monocular 3D object detection or 6DoF pose estimation. The most commonly used approach is to represent objects as 3D cuboids, and train a model to regress the cuboid parameters from a 2D RoI of the object [7, 8, 28, 41, 51, 54]. An alternative method involves predicting corresponding 3D points in an object coordinate space followed by pose estimation using 3D-2D alignment [11, 12, 20, 33, 49]. However, all these methods are limited to narrow datasets collected on a single camera and context with typically very few object classes. Existing models are also trained separately for every dataset, preventing them from being used more widely.",
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+        "text": "Recent work [7] takes a step towards scalability by introducing the Omni3D dataset, which aggregates 3D detection datasets from different domains, and trains a Cube R-CNN model to regress 3D bounding boxes for 50 classes. However, Omni3D notably lacks consistently oriented, object-centric boxes, as the ground truth orientations are not canonical for object classes. Cube R-CNN [7] instead uses a Chamfer distance loss which is invariant to the predicted 3D cuboid orientation. This causes it to predict inconsistent object orientations, for example, often flipping the orientations of cars by  $180^{\\circ}$ . Further, Omni3D is a detection dataset that does not provide object shape information.",
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+        "text": "Our work aims to overcome the above shortcomings, by providing a new large-scale dataset with consistent object-centric ground truth boxes along with detailed shape. We argue for the use of Normalized Object Coordinate Space (NOCS) as proposed in [49] as a 3D object shape representation. NOCS represents both the canonical orientation and the shape of the visible surface of the object, properties that are essential for real-world applications such as self-driving and robotics. It can also be used to estimate the 3D bounding box of the object. However, all existing work on NOCS [24, 25, 32, 48, 49] only train small models on small datasets with fewer than 10 classes. We address the lack of diverse NOCS datasets by proposing a large-scale monocular NOCS dataset, OmniNOCS that has NOCS annotations, instance segmentation, and canonically oriented bounding boxes for 97 object classes, containing  $380\\mathrm{k}$  images from 10 different data sources, making it the largest and most diverse NOCS dataset currently available. OmniNOCS includes all of the data from Omni3D (KITTI [19], nuScenes [10], ARKitScenes [5], Objectron [1], Hypersim [44], SUN-RGBD [45]), with the addition of Cityscapes [15], virtual KITTI [9], NOCS-Real275 [49], and the Waymo Open Dataset [46].",
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+        "type": "text",
+        "text": "Our work also introduces a new model, which we term \"NOCSformer\", that predicts NOCS coordinates and oriented 3D bounding boxes from monocular images and 2D detections for all the classes in OmniNOCS. NOCSformer leverages self-supervised pretrained ViTs [17, 40] and does not use any class-specific model parameters. This enables it scale to large vocabularies, and share information across semantically similar",
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+    {
+        "type": "header",
+        "text": "A. Krishnan et al.",
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+    {
+        "type": "text",
+        "text": "object classes. In contrast, existing NOCS models use small NOCS heads with class-specific parameters that significantly limit their performance on large datasets like OmniNOCS. NOCSformer also predicts the 3D size of the object along with its 3D orientation using a learned PnP head. This allows NOCSformer to predict 3D oriented bounding boxes and object point cloud in metric scale for diverse object categories. Our experiments evaluate the quality of NOCS and bounding boxes predicted by NOCSformer in comparison to existing NOCS prediction or 3D detection models. We find that training on OmniNOCS allows NOCSformer to generalize to unseen datasets, even outperforming baselines trained on the target dataset in NOCS prediction accuracy.",
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+        "text": "In summary, our contributions are:",
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+    {
+        "type": "list",
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+        "list_items": [
+            "- The OmniNOCS dataset: A new dataset containing Normalised Object Coordinates for 380k images in 97 categories, an order of magnitude larger on both counts than existing NOCS datasets.",
+            "- NOCSformer: A novel transformer-based architecture for predicting object NOCS, mask, and size from input 2D boxes, utilizing pre-trained self-supervised ViT backbones. NOCSformer is the first NOCS model to generalize to vocabularies with  $90+$  classes and to unseen datasets, including images from internet collections.",
+            "- OmniNOCS benchmark: an evaluation framework with metrics for directly comparing different NOCS prediction algorithms on OmniNOCS, with baselines established via NOCSFormer."
+        ],
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+        "type": "text",
+        "text": "2 Related work",
+        "text_level": 1,
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+    },
+    {
+        "type": "text",
+        "text": "The task of predicting 3D object pose has been studied both in the context of camera/object pose estimation (6DoF) and 3D bounding box estimation (6DoF pose + 3DoF size). A further distinction can be made between methods that regress directly to object pose / bounding box parameters, and methods that compute per pixel coordinates or depth as an initial step. We review each of these paradigms in the sections below.",
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+        "type": "text",
+        "text": "2.1 3D localization by regressing bounding boxes",
+        "text_level": 1,
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+        "page_idx": 2
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+    {
+        "type": "text",
+        "text": "Direct approaches to 3D object detection and pose estimation involve networks that output rotation, translation and scale parameters directly. Several works have explored different parameterizations in this setting, e.g., PoseCNN [54] uses regression of translation via centre direction + distance maps (which enable detection even under occlusion), and quaternion representation of rotation. BB8 [41] uses segmentation followed by a CNN to regress to 2D projections of the  $8 \\times 3\\mathrm{D}$  bounding box corners. Multi-view monocular approaches have also been proposed, e.g., DETR3D [52] which uses DETR-style attention to reason about object interactions. Several techniques also make use of existing 2D bounding box predictors, either as an input to a 3D lifting approach [34], or as a constraint on 3D box predictions [38].",
+        "bbox": [
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+        "page_idx": 2
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+    {
+        "type": "text",
+        "text": "Another group of works focuses on predicting the alignment of 3D CAD models within various modalities: images [21, 29], videos [31, 35], or 3D scans [2, 3]. These approaches typically determine the object's 3D translation, rotation, and size (9DoF) and find a CAD model with a similar visual appearance.",
+        "bbox": [
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+        "page_idx": 2
+    },
+    {
+        "type": "header",
+        "text": "OmniNOCS",
+        "bbox": [
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+        "page_idx": 2
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+    {
+        "type": "page_number",
+        "text": "3",
+        "bbox": [
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+        "page_idx": 2
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+    {
+        "type": "text",
+        "text": "2.2 3D localization from model-to-image alignment",
+        "text_level": 1,
+        "bbox": [
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+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "An alternative approach to 3D localization is to first predict correspondence, either between views or to a normalized space, and then reason over redundant correspondences to establish pose. This has been done with both sparse and dense correspondences, e.g., AutoShape [33] makes use of sparse 2D to 3D correspondences, with a learned shape model and sparse 2D keypoints. [20] uses pairwise semantic correspondence from ViT, to find pose between a reference image and a sequence of targets.",
+        "bbox": [
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+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "Several approaches use dense correspondences and the idea of Normalised Object Coordinate Spaces, or NOCS. In the original NOCS work, Wang et al. [49] use RGB-D data and 3D-3D correspondences for pose estimation. Follow-up work [11] estimates pose using PnP variants on 2D-3D correspondences. While NOCS [49] used the average spatial dimensions of the object category to define the object frame, [53] showed that an instance-specific NOCS coordinate frame can be used alongside a predicted instance size for the same purpose. Other methods combine coordinate regression and direct approaches, e.g., [48], which uses direct regression based on dense correspondences.",
+        "bbox": [
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+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "Similar to our work, MonoRUun [11] lifts 2D detections to 3D object coordinates without explicit class supervision. They also use a novel self-supervised coordinate regression training loss which obviates the need for detailed 3D ground truth. However, their representation does not include masks and therefore does not provide explicit object shape information. Their evaluation is also limited to 3 classes (car, pedestrian, cyclist) on the KITTI-Object test set. M3D-RPN [8] also has a single 3D head for multiple classes, jointly generating 2D and 3D bounding box predictions, though without shape information, and similarly limited to car, pedestrian and cyclist classes on KITTI.",
+        "bbox": [
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+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "2.3 Monocular object localization / pose estimation datasets",
+        "text_level": 1,
+        "bbox": [
+            215,
+            517,
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+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "For 3D object detection from monocular RGB images, most existing works use a small number of classes on a single dataset. Cube R-CNN/Omni3D [7] contribute towards creating a general purpose monocular 3D object detector. Cube R-CNN performs well over six 3D datasets: KITTI, SUN RGB-D, ARKitScenes, Objectron, nuScenes and Hypersim. However, the method is class specific, with Cube R-CNN trained only on 50 classes, and unable to work in the open class setting.",
+        "bbox": [
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+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "Cube R-CNN also uses a Chamfer loss on the predicted 3D box corners to deal with the inconsistent coordinate frame annotations in the underlying Omni3D datasets. The lack of direct orientation supervision results in inconsistent orientation predictions (for example, the positive x axis may point to the front or the back of the car for different instances). This orientation inconsistency is problematic for methods that seek to build detailed 3D models of object categories as a downstream task [27, 39].",
+        "bbox": [
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+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "Other works are not limited to 3D boxes, and directly provide object shapes. These shapes come in the form of annotated point clouds (e.g., ScanNet [16]), or aligned CAD models (e.g., Scan2CAD [2], Pix3D [47], CAD-Estate [36]). Object point clouds are created by labeling points on scanned 3D scenes, a process that doesn't scale well to large datasets. Semi-automatically aligning CAD models is scale-able to a certain extent, but since it relies on retrieving existing models, the resulting shapes are rarely accurate, and the alignments are sensitive to deformations and movable parts. In contrast to these works, we propose a large-scale dataset of many different categories.",
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+    {
+        "type": "page_number",
+        "text": "4",
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+    {
+        "type": "header",
+        "text": "A. Krishnan et al.",
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+    {
+        "type": "table",
+        "img_path": "images/9b6ca27155eb41b8344abde494fd93de510b97ba9fb9e956ca22813c78cf0d75.jpg",
+        "table_caption": [],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Dataset</td><td>NOCS GT</td><td>Real</td><td>#Images</td><td>#Classes</td><td>#Instances</td></tr><tr><td>CAMERA25 [49]</td><td>✓</td><td>×</td><td>300k</td><td>6</td><td>184</td></tr><tr><td>NOCS-Real275 [49]</td><td>✓</td><td>✓</td><td>7k</td><td>6</td><td>24</td></tr><tr><td>Wild6D [18]</td><td>×</td><td>✓</td><td>1M</td><td>5</td><td>1.8K</td></tr><tr><td>OmniNOCS</td><td>✓</td><td>✓</td><td>380k</td><td>97</td><td>&gt;450k</td></tr></table>",
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+    {
+        "type": "text",
+        "text": "Table 1: Comparison to other NOCS datasets: Other existing NOCS datasets are limited in number of classes and instances. Note that Wild6D has 1M images, but these are used for self-supervised training, as it does not include NOCS ground truth (GT).",
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+    },
+    {
+        "type": "image",
+        "img_path": "images/438dbb5de78e5233772616e4da6306952f437af2f155c8c65d4cfe25df4e33e0.jpg",
+        "image_caption": [
+            "Fig. 2: Preparation of the OmniNOCS dataset: First, we ensure that object orientations are canonical across different all subsets of OmniNOCS. Next, we re-compute depth for datasets where depth is not available or is too noisy. Finally, we annotate objects with (pseudo) instance mask labels where ground truth masks are not available."
+        ],
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+    {
+        "type": "text",
+        "text": "3 OmniNOCS dataset",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "3.1 Data statistics",
+        "text_level": 1,
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+        "type": "text",
+        "text": "We create OmniNOCS, a first-of-its-kind large and diverse NOCS dataset comprising data from several classes and domains. OmniNOCS uses data from self-driving scenes [9, 15, 19, 46], indoor scenes [5, 44, 45, 49], and object-centric videos [1]. Each of these sources use cameras with different parameters, ranging from phone cameras to wide FOV cameras mounted on self-driving cars. The number of object instances per image also varies widely, from single-object images [1] to images with hundreds of objects [44, 46].",
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+    {
+        "type": "text",
+        "text": "OmniNOCS has 97 object categories across 380k images. This by far exceeds the diversity of existing NOCS datasets (with less than 6 categories as shown in Table 1). The number of instances exceeds that of other NOCS datasets by more than 2 orders of magnitude. It also contains more images with NOCS annotations (380k). Note that while Wild6D has 1M images, these do not have ground truth NOCS or pose annotations, and includes every frame of object-centric videos. OmniNOCS is also more diverse than the most diverse 3D detection dataset (Omni3D [7] with 98 categories). 307k images in OmniNOCS are from real world scenes and 73k are synthetic. 107k of the images in OmniNOCS are from outdoor scenes, 102k images are from complex indoor environments, and 140k are from object-centric videos.",
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+        "type": "text",
+        "text": "3.2 Data preparation",
+        "text_level": 1,
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+        "type": "text",
+        "text": "Computing the NOCS for objects in an image requires canonically oriented 3D object bounding boxes, depth, and instance segmentations. Since the datasets we use were originally only proposed for monocular 3D detection, many of them lack dense depth or instance segmentation annotations (highlighted entries in Table 2). Additionally, NOCS",
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+    {
+        "type": "header",
+        "text": "OmniNOCS",
+        "bbox": [
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+    {
+        "type": "page_number",
+        "text": "5",
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+    {
+        "type": "table",
+        "img_path": "images/8857892129c5c11d3852279137f8cbc2f147c75cdc8c922918ad788fa4998aef.jpg",
+        "table_caption": [],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>3D Dataset</td><td>Depth source</td><td>Instance mask source</td><td>#Images</td><td>#Classes</td></tr><tr><td>KITTI [19]</td><td>LiDAR</td><td>Human-labelled [23]</td><td>7.4k</td><td>7</td></tr><tr><td>SUN-RGBD [45]</td><td>Depth Camera</td><td>Segmentation model [26]</td><td>10k</td><td>72</td></tr><tr><td>Objectron [1]</td><td>NeRF</td><td>NeRF + Human label</td><td>132k</td><td>9</td></tr><tr><td>nuScenes [10]</td><td>LiDAR</td><td>Segmentation Model [14]</td><td>30k</td><td>9</td></tr><tr><td>Hypersim [44]</td><td>Synthetic</td><td>Synthetic</td><td>64k</td><td>31</td></tr><tr><td>ARKitScenes [5]</td><td>Depth Camera</td><td>Segmentation model [26]</td><td>60k</td><td>15</td></tr><tr><td>Cityscapes 3D [15]</td><td>Stereo</td><td>Human-labelled</td><td>3.4k</td><td>8</td></tr><tr><td>Virtual KITTI [9]</td><td>Synthetic</td><td>Synthetic</td><td>4.1k</td><td>3</td></tr><tr><td>NOCS-Real275 [49]</td><td>Depth camera</td><td>Human-labelled</td><td>7k</td><td>6</td></tr><tr><td>Waymo OD [46]</td><td>LiDAR</td><td>Human-labelled</td><td>62k</td><td>7</td></tr><tr><td>OmniNOCS</td><td></td><td></td><td>380k</td><td>97</td></tr></table>",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "Table 2: Constituent 3D detection datasets used in OmniNOCS: We augment many of these datasets with depth and masks, as they are missing in the original data (highlighted entries). In addition, we canonicalize the orientations of bounding boxes across all datasets. #Classes lists the number of classes we use from each dataset. Those above the dashed line are part of Omni3D [7].",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "also requires all instances within a category to have consistent orientation annotations. While this is certainly not true across different 3D detection datasets, it is also not true within some large synthetic datasets such as Hypersim. OmniNOCS aggregates several datasets, with the addition of 1) new depth estimated via Mip-NeRF reconstructions, 2) additional instance masks via segmentation models and human labels, 3) manual labelling of coordinate axes to give consistent object-centric coordinate frames. This enables NOCS to be computed for every image in the dataset. This multi-stage process for OmniNOCS creation is illustrated in Figure 2 and explained below. A summary of the resulting OmniNOCS dataset is provided in Table 2.",
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+    {
+        "type": "text",
+        "text": "Orientation canonicalization: Although the datasets we use contain oriented 3D bounding boxes, they vary in their level of canonicalization. Some datasets have their class-canonical orientations i.e., all instances of a particular class are oriented consistently within the dataset (for example, all cars in [19] have X axis pointing forward, and Z upwards). In such cases, we ensure that this canonicalization is consistent with all of OmniNOCS by applying a fixed class-specific offset orientation for the dataset. Datasets like Hypersim [44] have no class canonicalization at all, ie, although objects have tight bounding boxes, their XYZ axes directions are different for each instance. We manually label each object in such datasets to select the canonical orientation out of six possible orientations for the bounding box. More details on the labelling process are provided in the supplementary material. Some classes may have more than one choice for canonical orientations (due to symmetry), in which case an orientation is selected arbitrarily.",
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+    {
+        "type": "text",
+        "text": "Depth augmentation: For outdoor datasets, we use sparse depth from LiDAR if available. We recomputed the depth on Cityscapes using a state-of-the-art stereo depth model [55], as we found the depth from the original dataset to be noisy. Since Objectron [1] does not contain dense depth, we train Neural Radiance Fields [4, 37] for each video sequence in the dataset to obtain dense depth.",
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+    },
+    {
+        "type": "text",
+        "text": "Instance segmentation: Since many datasets we use were intended for 3D detection, they do not camera instance segmentations. We annotate ARKitScenes and SUNRGBD objects using instance masks from Segment-Anything (SAM) [26], prompting it with the",
+        "bbox": [
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+    },
+    {
+        "type": "page_number",
+        "text": "6",
+        "bbox": [
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+        "page_idx": 5
+    },
+    {
+        "type": "header",
+        "text": "A. Krishnan et al.",
+        "bbox": [
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+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "projected 3D bounding box. Although SUN-RGBD provides segmentation masks, we find that the SAM annotations are of superior quality. For Objectron, we create accurate masks by efficiently annotating in 3D space. Specifically, we apply the pipeline of [4] to each of the Objectron videos. We fuse the resulting NeRF depth-maps to create a mesh. We post-process the 3D mesh, and get rid of the redundant vertices while keeping the vertices of the object. Finally, we create the masks for multiple frames by measuring the distance between each pixel on the depth map and the object mesh: pixels whose depth is far from the surface of the mesh are discarded, a process which also handles occlusions. NOCS computation: As proposed in [49], the NOCS coordinate  $x_{nocs}$  for a point on the object surface is defined as:",
+        "bbox": [
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+        "page_idx": 6
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\nx _ {n o c s} = \\frac {1}{s} ^ {o b j} T _ {c a m} x _ {c a m} \\tag {1}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "where  ${}^{obj}T_{cam} = \\left[{}^{obj}R_{cam}\\right|^{obj}t_{cam}$  is the 6DoF transformation from the camera to the object frame,  $x_{cam}$  the 3D location of the point in the camera frame, and  $s$  is a scalar, the size of the diagonal of the object's tight bounding box. It can be interpreted as the object shape scaled to a box with a unit diagonal. For each image, starting from a 3D pointcloud  $(x_{cam})$  obtained from the depth, we collect points on each object  $(x_{cam}^i)$  using its 3D bounding box and instance mask, which can be transformed to the NOCS coordinate  $(x_{nocs}^i)$  using (1). We store NOCS as a 3-channel 2D map (i.e.  $x_{nocs}^i$  at its corresponding 2D location obtained by projecting  $x_{cam}^i$ ). Our final result is the OmniNOCS dataset which contains instance segmentations, NOCS maps, and 3D bounding boxes for objects across 97 classes.",
+        "bbox": [
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+        "page_idx": 6
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+    {
+        "type": "text",
+        "text": "4 NOCSformer model",
+        "text_level": 1,
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "We propose a novel architecture for monocular NOCS prediction termed \"NOCsformer\" trained on OmniNOCS. NOCsformer primarily uses self-attention layers, and is the largest trained monocular NOCS prediction model to date. NOCsformer also contains a 3D size head and a learned PnP head that are used to estimate a canonically oriented 3D object bounding box from the NOCS.",
+        "bbox": [
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+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "4.1 Model architecture",
+        "text_level": 1,
+        "bbox": [
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+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "As shown in Figure 3, the NOCSformer architecture comprises an image backbone, a NOCS head, a size head, and a learned PnP head.",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "**Backbone:** Our backbone is a frozen Vision Transformer (ViT) [17], that uses DINOv2 weights [40] from self-supervised pretraining. This choice is motivated by recent findings on using frozen DINOv2 backbones for understanding depth, multi-view correspondences, or relative pose [20, 56]. Additionally, we use a Dense Prediction Transformer (DPT) [43] architecture to fuse low-resolution DINOv2 representations from multiple intermediate layers and upsample them by a factor of 8. This higher feature resolution is desirable to improve predictions on smaller objects. We train the DPT layers while keeping the ViT layers frozen. We use 2D input boxes to sample the DPT features using",
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+        "page_idx": 6
+    },
+    {
+        "type": "header",
+        "text": "OmniNOCS",
+        "bbox": [
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+        "page_idx": 6
+    },
+    {
+        "type": "page_number",
+        "text": "7",
+        "bbox": [
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+        "page_idx": 6
+    },
+    {
+        "type": "image",
+        "img_path": "images/54ac7709470a5519de97dce65bee88326729adc990e8400865551bc77a2d6051.jpg",
+        "image_caption": [
+            "Fig. 3: Architecture for NOCSformer: We use a transformer backbone to extract features from the input image, pool them using the 2D box RoIs, and feed the per-RoI features to the NOCS and size heads. Our novel NOCS head jointly predicts the NOCS and instance mask for the RoI. Our learned PnP head for pose estimation uses the predicted NOCS and instance mask to predict the projected 3D coordinate and 3D rotation of the object."
+        ],
+        "image_footnote": [],
+        "bbox": [
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+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "RoIAlign [22] into a 28x28 fixed resolution grid that is fed to the NOCS and size heads. While DPT has been previously leveraged for tasks that provide dense supervision, such as semantic segmentation and depth prediction, we use it in an instance-level prediction context, training it using supervision from object RoIs.",
+        "bbox": [
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+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "NOCS head: All existing NOCS prediction models [32, 48, 49] use MaskRCNN heads [22] with convolutional layers, in some cases with a separate head for each NOCS coordinate [49]. While this works on small datasets, we find that it significantly limits performance on OmniNOCS. NOCSformer therefore uses a simple (but large) transformer NOCS head with 10 self-attention blocks and one final upsampling conv layer to jointly predict both the instance mask and the NOCS map in the same output head. Similar to [49], for each NOCS coordinate, we predict logits over non-overlapping bins, and use the softmax to obtain the final NOCS value. Our ablations show that this choice works best on our challenging OmniNOCS dataset.",
+        "bbox": [
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+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "Size and learned PnP heads: We use a size head to predict the 3D size to scale the NOCS predictions to metric object coordinates. Our size head also uses self-attention blocks. Contrary to previous work [7,28,49], it does not use any class-specific layers or per-class average size statistics, as these limit scalability to larger and diverse vocabularies. Our learned PnP head is inspired from [32,48] and uses convolutional layers to predict the 3D orientation and a 2D projection of the 3D centroid. The orientation is predicted using the 6D partial rotation matrix representation [57] in allocentric space [7,28].",
+        "bbox": [
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+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "4.2 Localization from NOCS predictions",
+        "text_level": 1,
+        "bbox": [
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+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "To localize the object in 3D, we first scale the predicted NOCS using the predicted size to obtain unnormalized metric object coordinates. NOCSformer's learned PnP head predicts the object orientation in allocentric space, which is converted to the egocentric frame using the predicted projection of the 3D centroid. Using the unnormalized 3D object coordinates, object orientation, and the 3D centroid ray, the object depth is estimated from the corresponding 2D points.",
+        "bbox": [
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+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "Alternatively, the 6DoF pose for the object can be solved directly (without using the learned PnP head) by solving a PnP problem using efficient solvers [30]. However, the orientations so obtained are known to be more sensitive to errors in NOCS coordinates, and less robust when compared to learned methods [32, 48].",
+        "bbox": [
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+        "page_idx": 7
+    },
+    {
+        "type": "page_number",
+        "text": "8",
+        "bbox": [
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+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "header",
+        "text": "A. Krishnan et al.",
+        "bbox": [
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+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "4.3 Losses",
+        "text_level": 1,
+        "bbox": [
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+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "When training NOCSformer, we supervise the NOCS, mask, 3D size, orientation and centroid predictions using appropriate losses. We supervise predicted masks using a simple L2 loss with respect to the ground truth instance mask. Since our NOCS head uses binned prediction where the final NOCS value is a softmax over non-overlapping bin logits, we use a combination of cross-entropy and regression losses.",
+        "bbox": [
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+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\n\\begin{array}{l} \\mathcal {L} _ {\\text {m a s k}} = \\left\\| \\operatorname {m a s k} _ {\\text {p r e d}} - \\operatorname {m a s k} _ {\\text {g t}} \\right\\| _ {2} \\\\ \\mathcal {L} _ {N O C S} = \\operatorname {s o f t m a x} \\mathrm {C E} (\\hat {\\mathbf {n}}, \\hat {\\mathbf {n}} _ {g t}) + | | \\mathbf {n} - \\mathbf {n} _ {g t} | | _ {1} \\\\ \\end{array}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
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+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "where  $\\hat{\\mathbf{n}}$  are the predicted logits over the discretized bins and  $\\mathbf{n}$  the continuous NOCS coordinate prediction.",
+        "bbox": [
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+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "In addition to the supervised loss above, we also use a variant of the self-supervised NOCS loss  $\\mathcal{L}_{ss}$  from [11] that minimizes the reprojection error of the predicted NOCS using the ground truth pose and predicted mask. More details about  $\\mathcal{L}_{ss}$  are in the supplementary material. The NOCS and mask losses are applied to the fixed resolution grid predictions. Since our NOCS ground truth can be sparse, it is applied at only those locations on the grid that have valid NOCS ground truth.",
+        "bbox": [
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+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "Since our 3D size head also uses binned prediction, we use a combination of softmax cross-entropy and L1 loss for supervision. Note that our size loss is normalized by the ground truth size, in order to penalize errors on smaller objects equally. For the learned PnP head, we also supervise the centroid and rotation predictions using L1 losses in their output space.",
+        "bbox": [
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+        "page_idx": 8
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\n\\begin{array}{l} \\mathcal {L} _ {\\text {s i z e}} = \\operatorname {s o f t m a x C E} (\\hat {\\mathbf {s}}, \\hat {\\mathbf {s}} _ {g t}) + | \\mathbf {s} - \\mathbf {s} _ {\\mathbf {g} t} | / \\mathbf {s} _ {\\mathbf {g} t} \\\\ \\mathcal {L} _ {r o t} = \\left\\| ^ {c} \\mathbf {R} _ {o _ {p r e d}} - ^ {c} \\mathbf {R} _ {o _ {g t}} \\right\\| _ {1, 1} \\\\ \\mathcal {L} _ {\\text {c e n t r o i d}} = \\left\\| \\mathbf {c} _ {o} - \\mathbf {c} _ {o g t} \\right\\| _ {2} \\\\ \\mathcal {L} _ {P n P} = \\mathcal {L} _ {r o t} + \\mathcal {L} _ {c e n t r o i d} \\\\ \\end{array}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
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+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "The total loss for training NOCSformer is a weighted sum:",
+        "bbox": [
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+    {
+        "type": "equation",
+        "text": "\n$$\n\\mathcal {L} _ {\\text {t o t a l}} = w _ {\\text {s i z e}} \\mathcal {L} _ {\\text {s i z e}} + w _ {\\text {m a s k}} \\mathcal {L} _ {\\text {m a s k}} + w _ {\\text {N O C S}} \\mathcal {L} _ {\\text {N O C S}} + w _ {s s} \\mathcal {L} _ {s s} + w _ {P n P} \\mathcal {L} _ {P n P}\n$$\n",
+        "text_format": "latex",
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+        "type": "text",
+        "text": "5 Experiments",
+        "text_level": 1,
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+        "type": "text",
+        "text": "Although previous works predict NOCS accurately on a few categories [32, 48, 49], they only evaluate on 3D detection (localization) or pose estimation tasks, without quantifying the accuracy of the predicted NOCS. In section 5.1, we propose metrics and establish a benchmark to evaluate NOCS on the OmniNOCS dataset. We compare the localization accuracy of NOCSformer against existing benchmarks, by evaluating its localization accuracy on nuScenes [10] in section 5.2. We also evaluate the unique ability of NOCSformer to transfer to unseen datasets and domains. Finally, we provide ablations on critical design choices made in our model in section 5.4.",
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+    {
+        "type": "header",
+        "text": "OmniNOCS",
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+        "text": "9",
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+        "image_caption": [
+            "3D shape + pose"
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+        "image_caption": [
+            "Input",
+            "Fig. 4: Example results of our single unified NOCSformer model across various datasets and object classes. The left column shows input images and query 2D bounding boxes. The center column shows the NOCS+instance maps predicted by NOCSformer along with the estimated 3D pose overlaid on the input image. The NOCS can be used with the 3D boxes and object size to lift the objects to a 3D pointcloud, which is shown in the right column. Last row contains two examples."
+        ],
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+            "Output",
+            "3D shape + pose"
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+        "text": "10",
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+    {
+        "type": "header",
+        "text": "A. Krishnan et al.",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "Datasets: For our experiments, we train NOCSformer on the OmniNOCS dataset containing 97 classes, holding out images from the NOCS-Real275 dataset. We use NOCS-Real275 to evaluate NOCSformer's cross-dataset generalization capabilities by not training on it. NOCS-Real275 has 6 classes that overlap with the rest of OmniNOCS, although they are not the top 20 classes. It also differs from the rest of OmniNOCS in terms of camera parameters and the context of objects in the scene.",
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+    {
+        "type": "text",
+        "text": "5.1 NOCS prediction accuracy",
+        "text_level": 1,
+        "bbox": [
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+        "page_idx": 10
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+    {
+        "type": "text",
+        "text": "We propose the use of NOCS mAE and NOCS mPSNR to evaluate the accuracy of predicted NOCS. For each object the mean Absolute Error (mAE) and mPSNR are computed for all points in the intersection of ground truth and predicted masks, and reported as a mean of per-category means. However, the NOCS mAE/mPSNR do not penalize undersegmentations / sparse predictions. Since we also would like the NOCS predictions to span the full visible instance, we also evaluate the 2D mask mIoU.",
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+    {
+        "type": "text",
+        "text": "We evaluate NOCSformer on a subset of OmniNOCS that contains 75 classes with accurate ground truth NOCS and masks, see Table 3. It is able to predict NOCS with errors less than  $9\\%$  (0.089 mAE on OmniNOCS). We also evaluate on the held-out NOCS-Real275 dataset, which was not used to train NOCSformer. We find that the zero-shot NOCSformer outperforms the NOCS baseline from [49] that was fully trained on this dataset, a strong indication of the generalization capabilities of NOCSformer.",
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+    {
+        "type": "text",
+        "text": "5.2 3D localization accuracy",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "NOCS provides dense 3D-2D correspondences that can be used to estimate the 3D object oriented bounding box. This is done by using the predicted object size and solving a PnP problem, as explained in Sec. 4.2. We evaluate the accuracy of our estimated bounding boxes in different settings:",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "Outdoor scenes: We compare NOCSformer's 3D localization accuracy to that of other 3D detection models using the nuScenes true positive localization metrics on the challenging nuScenes dataset (included in OmniNOCS). We compare to Cube R-CNN, since it is the only other model that generalizes across diverse datasets. However, Cube R-CNN has two notable differences to NOCSformer: 1) being a detection model, it also jointly detects 2D object regions, and 2) it uses Chamfer loss causing it to have high orientation errors. As a more comparable baseline, we use a variant of our model (termed \"Cubeformer\"), by replacing our NOCS head with the cube head from Cube R-CNN",
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+    {
+        "type": "table",
+        "img_path": "images/b77edfab2528231a68f4af343c465b429d28c40490ef491c9b373dfe85d7054a.jpg",
+        "table_caption": [],
+        "table_footnote": [],
+        "table_body": "<table><tr><td rowspan=\"2\">Method</td><td colspan=\"3\">NOCS-Real275</td><td colspan=\"3\">OmniNOCS</td></tr><tr><td>NOCS MAE↓</td><td>NOCS PSNR↑</td><td>Mask IoU↑</td><td>NOCS MAE↓</td><td>NOCS PSNR↑</td><td>Mask IoU↑</td></tr><tr><td>NOCS baseline [49]</td><td>0.121</td><td>16.345</td><td>86.10</td><td>-</td><td>-</td><td>-</td></tr><tr><td>NOCSformer</td><td>0.107</td><td>18.527</td><td>89.03</td><td>0.094</td><td>20.245</td><td>78.50</td></tr></table>",
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+    {
+        "type": "text",
+        "text": "Table 3: NOCS and mask prediction evaluation: We report metrics for NOCSformer on 75 classes of OmniNOCS: it is the first model that is capable on predicting NOCS on such diverse data. Additionally, NOCSformer outperforms [49] which trained on NOCS-Real275, without training on NOCS-Real275.",
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+    },
+    {
+        "type": "header",
+        "text": "OmniNOCS",
+        "bbox": [
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+        "page_idx": 10
+    },
+    {
+        "type": "page_number",
+        "text": "11",
+        "bbox": [
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+    {
+        "type": "table",
+        "img_path": "images/36b4c61456202fa8216ed3b716aa91001338343ab65fd800c04acd774d438344.jpg",
+        "table_caption": [],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Method</td><td>Multi-dataset</td><td>mATE (m)↓</td><td>mAOE (rad)↓</td><td>mASE (%)↓</td><td>mIoU↑</td></tr><tr><td>FCOS3D [51]</td><td>X</td><td>0.777</td><td>0.400</td><td>0.231</td><td>-</td></tr><tr><td>PGD [50]</td><td>X</td><td>0.675</td><td>0.399</td><td>0.236</td><td>-</td></tr><tr><td>EProPNP [12]</td><td>X</td><td>0.676</td><td>0.363</td><td>0.263</td><td>-</td></tr><tr><td>EProPNP + TTA [12]</td><td>X</td><td>0.653</td><td>0.319</td><td>0.255</td><td>-</td></tr><tr><td>Cube R-CNN [7]</td><td>✓</td><td>0.650</td><td>1.305</td><td>0.283</td><td>0.349</td></tr><tr><td>Cubeformer</td><td>✓</td><td>0.790</td><td>0.573</td><td>0.301</td><td>0.280</td></tr><tr><td>NOCSformer</td><td>✓</td><td>0.887</td><td>0.558</td><td>0.291</td><td>0.377</td></tr></table>",
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+        "page_idx": 11
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+    {
+        "type": "text",
+        "text": "without a Chamfer loss. It uses the input 2D boxes. More details about Cubeformer are in the supplementary material. From Table 4, we find that NOCSformer's box orientations are canonical and more accurate than both Cube R-CNN and Cubeformer. Moreover, the boxes estimated from NOCSformer's NOCS predictions are comparable in translation and scale errors to those of Cube R-CNN and Cubeformer, even though it does not directly regress depth. While all 3 methods generalize across several classes and domains, baselines trained solely on nuScenes localization (top half) significantly outperform them on this task.",
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+    {
+        "type": "text",
+        "text": "Cross-dataset generalization (indoor): Here, we hold out NOCS-Real275 from OmniNOCS when training NOCSformer, and use it to evaluate NOCSformer's ability to generalize to unseen domains. NOCS-Real275 features a tabletop setting with multiple objects, which is semantically different from our other indoor datasets. We compare against Cube R-CNN [7] and the NOCS [49] model that was trained from scratch on this dataset alone. [49] also uses class-specific model parameters and additional losses for symmetric objects that do not scale to larger vocabularies.",
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "The results are shown in Table 5. We find that NOCSformer is more accurate at transferring to this unseen dataset compared to Cube R-CNN, indicating the generalizability of NOCS-based localization methods over that of box-regression methods. Note that the mAP metrics used in the NOCS-Real275 datasets may also be affected by the false positives/negatives from Cube R-CNN. However, both models are worse than the supervised NOCS baseline.",
+        "bbox": [
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+        "page_idx": 11
+    },
+    {
+        "type": "table",
+        "img_path": "images/b486b9e61039fc93e7a94311ff611443e26d0ab2c993cb545b7e318b1b450033.jpg",
+        "table_caption": [
+            "Table 4: Outdoor localization: Comparison of 3D localization accuracy on the nuScenes subset of OmniNOCS. Top half shows methods that are only trained on nuScenes: while these perform better on nuScenes itself, they do not generalize to other datasets and classes. OmniNOCS is competitive with Cube R-CNN on mIoU while also predicting canonical orientations and object coordinates. Note that mIoU and orientation error only uses the predicted yaw orientation component."
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td rowspan=\"2\">Method</td><td rowspan=\"2\">Transferred?</td><td rowspan=\"2\">Depth input?</td><td colspan=\"2\">mAP</td></tr><tr><td>3D IoU @ 25</td><td>3D IoU @ 50</td></tr><tr><td>NOCS model [49]</td><td>X</td><td>✓</td><td>79.6</td><td>72.4</td></tr><tr><td>CubeRCNN [7]</td><td>✓</td><td>X</td><td>14.9</td><td>4.1</td></tr><tr><td>NOCSformer</td><td>✓</td><td>X</td><td>43.5</td><td>10.6</td></tr></table>",
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+    {
+        "type": "text",
+        "text": "Table 5: Cross-dataset generalization: Comparison of localization accuracy on NOCS-Real275 for all classes, using mAP at different thresholds. This dataset has been held out when training CubeRCNN and NOCSformer. NOCSformer is able to generalize to NOCS-Real275 without any additional training.",
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+    {
+        "type": "page_number",
+        "text": "12",
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+    {
+        "type": "header",
+        "text": "A. Krishnan et al.",
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+        "page_idx": 11
+    },
+    {
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+        "img_path": "images/7c23736e5a82817d3573db53649829c2d26c417528908f20903bc18b98a89633.jpg",
+        "image_caption": [
+            "Fig. 5: Generalization across datasets: NOCSformer can generalize to new datasets that present new camera models and object domains. We show this zero-shot capability of NOCSformer (bottom row) on the NOCS-Real275 test set without training on NOCS-Real275 dataset. The predictions from a NOCS baseline [49] trained explicitly on the NOCS-Real275 dataset are shown in the top row."
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+    {
+        "type": "text",
+        "text": "5.3 3D orientation accuracy",
+        "text_level": 1,
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+        "text": "A key challenge for 3D localization models trained on multiple datasets [7] is predicting canonical object orientations, as these datasets typically differ in their object pose conventions. OmniNOCS ensures that object poses in all constituent datasets are consistent within a category, enabling our model to predict canonical orientations. We evaluate this by computing the accuracy at different orientation error threshold on KITTI-val [13] in Table 6. We compare the accuracy of our orientation predictions to those of Cube R-CNN [7], which are trained using Chamfer loss, and therefore suffers at predicting canonical orientations. We also compare against our Cubeformer baseline that directly supervises the orientations. We find that orientations estimated using NOCSformer are more consistent and accurate than both our baselines.",
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+        "table_body": "<table><tr><td rowspan=\"2\">Method</td><td colspan=\"3\">Gravity axis</td><td colspan=\"3\">Heading axis (X)</td></tr><tr><td>Accuracy @ 1 deg</td><td>Accuracy @ 5 deg</td><td>Accuracy @ 90 deg</td><td>Accuracy 5 deg</td><td>Accuracy @ 10 deg</td><td>Accuracy @ 90 deg</td></tr><tr><td>CubeRCNN</td><td>5.75</td><td>18.29</td><td>51.74</td><td>19.43</td><td>21.55</td><td>34.17</td></tr><tr><td>Cubeformer</td><td>80.79</td><td>98.52</td><td>100.0</td><td>41.99</td><td>55.88</td><td>77.26</td></tr><tr><td>NOCSformer</td><td>84.24</td><td>99.95</td><td>100.0</td><td>49.16</td><td>59.65</td><td>81.99</td></tr></table>",
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+        "text": "Table 6: 3D orientation accuracy for models trained on multiple datasets: Models that use Chamfer distance (CubeRCNN) for supervising orientation heads end up being inconsistent in their orientation predictions. The results are averaged over 5 classes in the KITTI-val subset [13].",
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+        "type": "header",
+        "text": "OmniNOCS",
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+        "table_body": "<table><tr><td>Architecture</td><td>NOCS PSNR↑</td><td>Mask IoU↑</td></tr><tr><td>NOCSformer</td><td>20.431</td><td>81.69</td></tr><tr><td>Hourglass head</td><td>-3.064</td><td>-3.16</td></tr><tr><td>MaskRCNN head</td><td>-5.23</td><td>-21.27</td></tr><tr><td>w/o discretized prediction</td><td>-4.11</td><td>-</td></tr></table>",
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+    {
+        "type": "text",
+        "text": "Table 7: Comparison of different architecture choices for the NOCS and mask prediction head: We experiment with transformer, hourglass and MaskRCNN architectures. We compare whether discretized prediction of NOCS is better than continuous regression for transformer heads. We quantify the difference in performance when using separate heads for mask and NOCS prediction as opposed to a single head.",
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+    {
+        "type": "text",
+        "text": "5.4 Ablations",
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+        "type": "text",
+        "text": "NOCS head architecture: While all existing architectures use a few CNN layers for their NOCS heads [49], we find that this significantly limits the NOCS prediction performance when scaling to more classes. Using our transformer NOCS head provides a 5.23dB improvement in NOCS PSNR and a  $21.27\\%$  improvement in mask mIoU. We also experiment with a larger convolutional head: the Hourglass model from [6]. We find that the NOCSformer NOCS heads are even better than Hourglass, with an improvement of 3.06dB on NOCS PSNR and  $3.16\\%$  on mask IoU (as shown in Table 7).",
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+        "type": "text",
+        "text": "Discretized versus continuous prediction of NOCS: NOCSformer models the final NOCS prediction as a classification layer with 50 non-overlapping bins. This was observed to be better than a linear regression layer with a MaskRCNN head in [49]. We find that it is also significantly better when using a transformer head, improving the NOCS prediction PSNR by 4.11 dB, as in Table 7.",
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+    {
+        "type": "text",
+        "text": "6 Conclusion and Future work",
+        "text_level": 1,
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+        "type": "text",
+        "text": "This paper has introduced a new large scale dataset of Normalized Object Coordinates (NOCS) for a wide variety of object classes in indoor and outdoor scenes. It has also proposed a single transformer-based model NOCSformer that can predict object 3D pose, size, and NOCS for any of these objects given 2D bounding box inputs. These allows our model to obtain 3D shape and oriented bounding boxes of objects in metric scale from a single input image. This represents the first attempt to generalize NOCS estimation beyond small datasets of narrow domain, increasing the number of object categories available by an order of magnitude. We hope this provides a means for others to explore large-scale monocular 3D object pose and shape estimation.",
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+        "type": "text",
+        "text": "Some limitations of our current method include the handling of symmetric objects, where the coordinate system has multiple possible solutions, and reflected geometry, such as left / right shoes. Future work could address these issues, for example, minimizing over multiple coordinate frames in the loss for symmetric objects, and potential estimation of left / right coordinate frames for reflected geometry.",
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+    {
+        "type": "header",
+        "text": "A. Krishnan et al.",
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+    {
+        "type": "text",
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2024/OmniNOCS_ A unified NOCS dataset and model for 3D lifting of 2D objects/full.md

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+# OmniNOCS: A unified NOCS dataset and model for 3D lifting of 2D objects
+
+Akshay Krishnan $^{1,2}$ , Abhijit Kundu $^{1}$ , Kevis-Kokitsi Maninis $^{1}$ , James Hays $^{2}$ , and Matthew Brown $^{1}$
+
+1 Google Research†
+
+2 Georgia Institute of Technology
+
+![](images/c6cdaaff917a5eba3946dee9b9ca5ebdc840d3a5a95ab822c2bb341eca8803cc.jpg)  
+Fig. 1: We introduce OmniNOCS, a large-scale dataset with Normalized Object Coordinates (NOCS), instance masks, and 3D box annotations for objects across several classes, domains, and cameras. We also propose NOCSformer, a model trained on OmniNOCS that lifts each 2D object bounding box in an image to its corresponding 3D oriented box (pose) and 3D pointcloud (shape).
+
+![](images/6fd026d4e6665924bd49b1a033471c917353d1a4d0c01170f5b7c1310616d6f7.jpg)
+
+![](images/6a0d92c0efe8f14ba295dcc2483152ac8b33510ea8aef85261200f64e80b54f2.jpg)
+
+Abstract. We propose OmniNOCS, a large-scale monocular dataset with 3D Normalized Object Coordinate Space (NOCS) maps, object masks, and 3D bounding box annotations for indoor and outdoor scenes. OmniNOCS has 20 times more object classes and 200 times more instances than existing NOCS datasets (NOCS-Real275, Wild6D). We use OmniNOCS to train a novel, transformer-based monocular NOCS prediction model (NOCsformer) that can predict accurate NOCS, instance masks and poses from 2D object detections across diverse classes. It is the first NOCS model that can generalize to a broad range of classes when prompted with 2D boxes. We evaluate our model on the task of 3D oriented bounding box prediction, where it achieves comparable results to state-of-the-art 3D detection methods such as Cube R-CNN. Unlike other 3D detection methods, our model also provides detailed and accurate 3D object shape and segmentation. We propose a novel benchmark for the task of NOCS prediction based on OmniNOCS, which we hope will serve as a useful baseline for future work in this area. Our dataset and code will be at the project website: https://omninocs.github.io.
+
+# 1 Introduction
+
+Predicting the 6 Degree-of-Freedom (6DoF) pose and shape of objects from images is a crucial problem in 3D scene understanding. Robots need to understand the location,
+
+shape, and orientation of various objects to grasp and interact with them. Self-driving vehicles need to understand the location and heading of vehicles, pedestrians, and other objects on the road. In particular, the 3D orientation of these objects is crucial to predict their behavior. It is also important in AR/VR applications, as it allows users to interact with objects in meaningful ways. Predicting object shape and pose from monocular images is also a prerequisite to initialize 3D panoptic neural scene graphs [27, 39] and to track objects in videos [42]. These applications require approaches that generalize across a wide range of object classes, environments, and camera models.
+
+The problem of localizing 3D objects has been extensively studied through the lens of monocular 3D object detection or 6DoF pose estimation. The most commonly used approach is to represent objects as 3D cuboids, and train a model to regress the cuboid parameters from a 2D RoI of the object [7, 8, 28, 41, 51, 54]. An alternative method involves predicting corresponding 3D points in an object coordinate space followed by pose estimation using 3D-2D alignment [11, 12, 20, 33, 49]. However, all these methods are limited to narrow datasets collected on a single camera and context with typically very few object classes. Existing models are also trained separately for every dataset, preventing them from being used more widely.
+
+Recent work [7] takes a step towards scalability by introducing the Omni3D dataset, which aggregates 3D detection datasets from different domains, and trains a Cube R-CNN model to regress 3D bounding boxes for 50 classes. However, Omni3D notably lacks consistently oriented, object-centric boxes, as the ground truth orientations are not canonical for object classes. Cube R-CNN [7] instead uses a Chamfer distance loss which is invariant to the predicted 3D cuboid orientation. This causes it to predict inconsistent object orientations, for example, often flipping the orientations of cars by  $180^{\circ}$ . Further, Omni3D is a detection dataset that does not provide object shape information.
+
+Our work aims to overcome the above shortcomings, by providing a new large-scale dataset with consistent object-centric ground truth boxes along with detailed shape. We argue for the use of Normalized Object Coordinate Space (NOCS) as proposed in [49] as a 3D object shape representation. NOCS represents both the canonical orientation and the shape of the visible surface of the object, properties that are essential for real-world applications such as self-driving and robotics. It can also be used to estimate the 3D bounding box of the object. However, all existing work on NOCS [24, 25, 32, 48, 49] only train small models on small datasets with fewer than 10 classes. We address the lack of diverse NOCS datasets by proposing a large-scale monocular NOCS dataset, OmniNOCS that has NOCS annotations, instance segmentation, and canonically oriented bounding boxes for 97 object classes, containing  $380\mathrm{k}$  images from 10 different data sources, making it the largest and most diverse NOCS dataset currently available. OmniNOCS includes all of the data from Omni3D (KITTI [19], nuScenes [10], ARKitScenes [5], Objectron [1], Hypersim [44], SUN-RGBD [45]), with the addition of Cityscapes [15], virtual KITTI [9], NOCS-Real275 [49], and the Waymo Open Dataset [46].
+
+Our work also introduces a new model, which we term "NOCSformer", that predicts NOCS coordinates and oriented 3D bounding boxes from monocular images and 2D detections for all the classes in OmniNOCS. NOCSformer leverages self-supervised pretrained ViTs [17, 40] and does not use any class-specific model parameters. This enables it scale to large vocabularies, and share information across semantically similar
+
+object classes. In contrast, existing NOCS models use small NOCS heads with class-specific parameters that significantly limit their performance on large datasets like OmniNOCS. NOCSformer also predicts the 3D size of the object along with its 3D orientation using a learned PnP head. This allows NOCSformer to predict 3D oriented bounding boxes and object point cloud in metric scale for diverse object categories. Our experiments evaluate the quality of NOCS and bounding boxes predicted by NOCSformer in comparison to existing NOCS prediction or 3D detection models. We find that training on OmniNOCS allows NOCSformer to generalize to unseen datasets, even outperforming baselines trained on the target dataset in NOCS prediction accuracy.
+
+In summary, our contributions are:
+
+- The OmniNOCS dataset: A new dataset containing Normalised Object Coordinates for 380k images in 97 categories, an order of magnitude larger on both counts than existing NOCS datasets.  
+- NOCSformer: A novel transformer-based architecture for predicting object NOCS, mask, and size from input 2D boxes, utilizing pre-trained self-supervised ViT backbones. NOCSformer is the first NOCS model to generalize to vocabularies with  $90+$  classes and to unseen datasets, including images from internet collections.  
+- OmniNOCS benchmark: an evaluation framework with metrics for directly comparing different NOCS prediction algorithms on OmniNOCS, with baselines established via NOCSFormer.
+
+# 2 Related work
+
+The task of predicting 3D object pose has been studied both in the context of camera/object pose estimation (6DoF) and 3D bounding box estimation (6DoF pose + 3DoF size). A further distinction can be made between methods that regress directly to object pose / bounding box parameters, and methods that compute per pixel coordinates or depth as an initial step. We review each of these paradigms in the sections below.
+
+# 2.1 3D localization by regressing bounding boxes
+
+Direct approaches to 3D object detection and pose estimation involve networks that output rotation, translation and scale parameters directly. Several works have explored different parameterizations in this setting, e.g., PoseCNN [54] uses regression of translation via centre direction + distance maps (which enable detection even under occlusion), and quaternion representation of rotation. BB8 [41] uses segmentation followed by a CNN to regress to 2D projections of the  $8 \times 3\mathrm{D}$  bounding box corners. Multi-view monocular approaches have also been proposed, e.g., DETR3D [52] which uses DETR-style attention to reason about object interactions. Several techniques also make use of existing 2D bounding box predictors, either as an input to a 3D lifting approach [34], or as a constraint on 3D box predictions [38].
+
+Another group of works focuses on predicting the alignment of 3D CAD models within various modalities: images [21, 29], videos [31, 35], or 3D scans [2, 3]. These approaches typically determine the object's 3D translation, rotation, and size (9DoF) and find a CAD model with a similar visual appearance.
+
+# 2.2 3D localization from model-to-image alignment
+
+An alternative approach to 3D localization is to first predict correspondence, either between views or to a normalized space, and then reason over redundant correspondences to establish pose. This has been done with both sparse and dense correspondences, e.g., AutoShape [33] makes use of sparse 2D to 3D correspondences, with a learned shape model and sparse 2D keypoints. [20] uses pairwise semantic correspondence from ViT, to find pose between a reference image and a sequence of targets.
+
+Several approaches use dense correspondences and the idea of Normalised Object Coordinate Spaces, or NOCS. In the original NOCS work, Wang et al. [49] use RGB-D data and 3D-3D correspondences for pose estimation. Follow-up work [11] estimates pose using PnP variants on 2D-3D correspondences. While NOCS [49] used the average spatial dimensions of the object category to define the object frame, [53] showed that an instance-specific NOCS coordinate frame can be used alongside a predicted instance size for the same purpose. Other methods combine coordinate regression and direct approaches, e.g., [48], which uses direct regression based on dense correspondences.
+
+Similar to our work, MonoRUun [11] lifts 2D detections to 3D object coordinates without explicit class supervision. They also use a novel self-supervised coordinate regression training loss which obviates the need for detailed 3D ground truth. However, their representation does not include masks and therefore does not provide explicit object shape information. Their evaluation is also limited to 3 classes (car, pedestrian, cyclist) on the KITTI-Object test set. M3D-RPN [8] also has a single 3D head for multiple classes, jointly generating 2D and 3D bounding box predictions, though without shape information, and similarly limited to car, pedestrian and cyclist classes on KITTI.
+
+# 2.3 Monocular object localization / pose estimation datasets
+
+For 3D object detection from monocular RGB images, most existing works use a small number of classes on a single dataset. Cube R-CNN/Omni3D [7] contribute towards creating a general purpose monocular 3D object detector. Cube R-CNN performs well over six 3D datasets: KITTI, SUN RGB-D, ARKitScenes, Objectron, nuScenes and Hypersim. However, the method is class specific, with Cube R-CNN trained only on 50 classes, and unable to work in the open class setting.
+
+Cube R-CNN also uses a Chamfer loss on the predicted 3D box corners to deal with the inconsistent coordinate frame annotations in the underlying Omni3D datasets. The lack of direct orientation supervision results in inconsistent orientation predictions (for example, the positive x axis may point to the front or the back of the car for different instances). This orientation inconsistency is problematic for methods that seek to build detailed 3D models of object categories as a downstream task [27, 39].
+
+Other works are not limited to 3D boxes, and directly provide object shapes. These shapes come in the form of annotated point clouds (e.g., ScanNet [16]), or aligned CAD models (e.g., Scan2CAD [2], Pix3D [47], CAD-Estate [36]). Object point clouds are created by labeling points on scanned 3D scenes, a process that doesn't scale well to large datasets. Semi-automatically aligning CAD models is scale-able to a certain extent, but since it relies on retrieving existing models, the resulting shapes are rarely accurate, and the alignments are sensitive to deformations and movable parts. In contrast to these works, we propose a large-scale dataset of many different categories.
+
+<table><tr><td>Dataset</td><td>NOCS GT</td><td>Real</td><td>#Images</td><td>#Classes</td><td>#Instances</td></tr><tr><td>CAMERA25 [49]</td><td>✓</td><td>×</td><td>300k</td><td>6</td><td>184</td></tr><tr><td>NOCS-Real275 [49]</td><td>✓</td><td>✓</td><td>7k</td><td>6</td><td>24</td></tr><tr><td>Wild6D [18]</td><td>×</td><td>✓</td><td>1M</td><td>5</td><td>1.8K</td></tr><tr><td>OmniNOCS</td><td>✓</td><td>✓</td><td>380k</td><td>97</td><td>&gt;450k</td></tr></table>
+
+Table 1: Comparison to other NOCS datasets: Other existing NOCS datasets are limited in number of classes and instances. Note that Wild6D has 1M images, but these are used for self-supervised training, as it does not include NOCS ground truth (GT).
+
+![](images/438dbb5de78e5233772616e4da6306952f437af2f155c8c65d4cfe25df4e33e0.jpg)  
+Fig. 2: Preparation of the OmniNOCS dataset: First, we ensure that object orientations are canonical across different all subsets of OmniNOCS. Next, we re-compute depth for datasets where depth is not available or is too noisy. Finally, we annotate objects with (pseudo) instance mask labels where ground truth masks are not available.
+
+# 3 OmniNOCS dataset
+
+# 3.1 Data statistics
+
+We create OmniNOCS, a first-of-its-kind large and diverse NOCS dataset comprising data from several classes and domains. OmniNOCS uses data from self-driving scenes [9, 15, 19, 46], indoor scenes [5, 44, 45, 49], and object-centric videos [1]. Each of these sources use cameras with different parameters, ranging from phone cameras to wide FOV cameras mounted on self-driving cars. The number of object instances per image also varies widely, from single-object images [1] to images with hundreds of objects [44, 46].
+
+OmniNOCS has 97 object categories across 380k images. This by far exceeds the diversity of existing NOCS datasets (with less than 6 categories as shown in Table 1). The number of instances exceeds that of other NOCS datasets by more than 2 orders of magnitude. It also contains more images with NOCS annotations (380k). Note that while Wild6D has 1M images, these do not have ground truth NOCS or pose annotations, and includes every frame of object-centric videos. OmniNOCS is also more diverse than the most diverse 3D detection dataset (Omni3D [7] with 98 categories). 307k images in OmniNOCS are from real world scenes and 73k are synthetic. 107k of the images in OmniNOCS are from outdoor scenes, 102k images are from complex indoor environments, and 140k are from object-centric videos.
+
+# 3.2 Data preparation
+
+Computing the NOCS for objects in an image requires canonically oriented 3D object bounding boxes, depth, and instance segmentations. Since the datasets we use were originally only proposed for monocular 3D detection, many of them lack dense depth or instance segmentation annotations (highlighted entries in Table 2). Additionally, NOCS
+
+<table><tr><td>3D Dataset</td><td>Depth source</td><td>Instance mask source</td><td>#Images</td><td>#Classes</td></tr><tr><td>KITTI [19]</td><td>LiDAR</td><td>Human-labelled [23]</td><td>7.4k</td><td>7</td></tr><tr><td>SUN-RGBD [45]</td><td>Depth Camera</td><td>Segmentation model [26]</td><td>10k</td><td>72</td></tr><tr><td>Objectron [1]</td><td>NeRF</td><td>NeRF + Human label</td><td>132k</td><td>9</td></tr><tr><td>nuScenes [10]</td><td>LiDAR</td><td>Segmentation Model [14]</td><td>30k</td><td>9</td></tr><tr><td>Hypersim [44]</td><td>Synthetic</td><td>Synthetic</td><td>64k</td><td>31</td></tr><tr><td>ARKitScenes [5]</td><td>Depth Camera</td><td>Segmentation model [26]</td><td>60k</td><td>15</td></tr><tr><td>Cityscapes 3D [15]</td><td>Stereo</td><td>Human-labelled</td><td>3.4k</td><td>8</td></tr><tr><td>Virtual KITTI [9]</td><td>Synthetic</td><td>Synthetic</td><td>4.1k</td><td>3</td></tr><tr><td>NOCS-Real275 [49]</td><td>Depth camera</td><td>Human-labelled</td><td>7k</td><td>6</td></tr><tr><td>Waymo OD [46]</td><td>LiDAR</td><td>Human-labelled</td><td>62k</td><td>7</td></tr><tr><td>OmniNOCS</td><td></td><td></td><td>380k</td><td>97</td></tr></table>
+
+Table 2: Constituent 3D detection datasets used in OmniNOCS: We augment many of these datasets with depth and masks, as they are missing in the original data (highlighted entries). In addition, we canonicalize the orientations of bounding boxes across all datasets. #Classes lists the number of classes we use from each dataset. Those above the dashed line are part of Omni3D [7].
+
+also requires all instances within a category to have consistent orientation annotations. While this is certainly not true across different 3D detection datasets, it is also not true within some large synthetic datasets such as Hypersim. OmniNOCS aggregates several datasets, with the addition of 1) new depth estimated via Mip-NeRF reconstructions, 2) additional instance masks via segmentation models and human labels, 3) manual labelling of coordinate axes to give consistent object-centric coordinate frames. This enables NOCS to be computed for every image in the dataset. This multi-stage process for OmniNOCS creation is illustrated in Figure 2 and explained below. A summary of the resulting OmniNOCS dataset is provided in Table 2.
+
+Orientation canonicalization: Although the datasets we use contain oriented 3D bounding boxes, they vary in their level of canonicalization. Some datasets have their class-canonical orientations i.e., all instances of a particular class are oriented consistently within the dataset (for example, all cars in [19] have X axis pointing forward, and Z upwards). In such cases, we ensure that this canonicalization is consistent with all of OmniNOCS by applying a fixed class-specific offset orientation for the dataset. Datasets like Hypersim [44] have no class canonicalization at all, ie, although objects have tight bounding boxes, their XYZ axes directions are different for each instance. We manually label each object in such datasets to select the canonical orientation out of six possible orientations for the bounding box. More details on the labelling process are provided in the supplementary material. Some classes may have more than one choice for canonical orientations (due to symmetry), in which case an orientation is selected arbitrarily.
+
+Depth augmentation: For outdoor datasets, we use sparse depth from LiDAR if available. We recomputed the depth on Cityscapes using a state-of-the-art stereo depth model [55], as we found the depth from the original dataset to be noisy. Since Objectron [1] does not contain dense depth, we train Neural Radiance Fields [4, 37] for each video sequence in the dataset to obtain dense depth.
+
+Instance segmentation: Since many datasets we use were intended for 3D detection, they do not camera instance segmentations. We annotate ARKitScenes and SUNRGBD objects using instance masks from Segment-Anything (SAM) [26], prompting it with the
+
+projected 3D bounding box. Although SUN-RGBD provides segmentation masks, we find that the SAM annotations are of superior quality. For Objectron, we create accurate masks by efficiently annotating in 3D space. Specifically, we apply the pipeline of [4] to each of the Objectron videos. We fuse the resulting NeRF depth-maps to create a mesh. We post-process the 3D mesh, and get rid of the redundant vertices while keeping the vertices of the object. Finally, we create the masks for multiple frames by measuring the distance between each pixel on the depth map and the object mesh: pixels whose depth is far from the surface of the mesh are discarded, a process which also handles occlusions. NOCS computation: As proposed in [49], the NOCS coordinate  $x_{nocs}$  for a point on the object surface is defined as:
+
+$$
+x _ {n o c s} = \frac {1}{s} ^ {o b j} T _ {c a m} x _ {c a m} \tag {1}
+$$
+
+where  ${}^{obj}T_{cam} = \left[{}^{obj}R_{cam}\right|^{obj}t_{cam}$  is the 6DoF transformation from the camera to the object frame,  $x_{cam}$  the 3D location of the point in the camera frame, and  $s$  is a scalar, the size of the diagonal of the object's tight bounding box. It can be interpreted as the object shape scaled to a box with a unit diagonal. For each image, starting from a 3D pointcloud  $(x_{cam})$  obtained from the depth, we collect points on each object  $(x_{cam}^i)$  using its 3D bounding box and instance mask, which can be transformed to the NOCS coordinate  $(x_{nocs}^i)$  using (1). We store NOCS as a 3-channel 2D map (i.e.  $x_{nocs}^i$  at its corresponding 2D location obtained by projecting  $x_{cam}^i$ ). Our final result is the OmniNOCS dataset which contains instance segmentations, NOCS maps, and 3D bounding boxes for objects across 97 classes.
+
+# 4 NOCSformer model
+
+We propose a novel architecture for monocular NOCS prediction termed "NOCsformer" trained on OmniNOCS. NOCsformer primarily uses self-attention layers, and is the largest trained monocular NOCS prediction model to date. NOCsformer also contains a 3D size head and a learned PnP head that are used to estimate a canonically oriented 3D object bounding box from the NOCS.
+
+# 4.1 Model architecture
+
+As shown in Figure 3, the NOCSformer architecture comprises an image backbone, a NOCS head, a size head, and a learned PnP head.
+
+**Backbone:** Our backbone is a frozen Vision Transformer (ViT) [17], that uses DINOv2 weights [40] from self-supervised pretraining. This choice is motivated by recent findings on using frozen DINOv2 backbones for understanding depth, multi-view correspondences, or relative pose [20, 56]. Additionally, we use a Dense Prediction Transformer (DPT) [43] architecture to fuse low-resolution DINOv2 representations from multiple intermediate layers and upsample them by a factor of 8. This higher feature resolution is desirable to improve predictions on smaller objects. We train the DPT layers while keeping the ViT layers frozen. We use 2D input boxes to sample the DPT features using
+
+![](images/54ac7709470a5519de97dce65bee88326729adc990e8400865551bc77a2d6051.jpg)  
+Fig. 3: Architecture for NOCSformer: We use a transformer backbone to extract features from the input image, pool them using the 2D box RoIs, and feed the per-RoI features to the NOCS and size heads. Our novel NOCS head jointly predicts the NOCS and instance mask for the RoI. Our learned PnP head for pose estimation uses the predicted NOCS and instance mask to predict the projected 3D coordinate and 3D rotation of the object.
+
+RoIAlign [22] into a 28x28 fixed resolution grid that is fed to the NOCS and size heads. While DPT has been previously leveraged for tasks that provide dense supervision, such as semantic segmentation and depth prediction, we use it in an instance-level prediction context, training it using supervision from object RoIs.
+
+NOCS head: All existing NOCS prediction models [32, 48, 49] use MaskRCNN heads [22] with convolutional layers, in some cases with a separate head for each NOCS coordinate [49]. While this works on small datasets, we find that it significantly limits performance on OmniNOCS. NOCSformer therefore uses a simple (but large) transformer NOCS head with 10 self-attention blocks and one final upsampling conv layer to jointly predict both the instance mask and the NOCS map in the same output head. Similar to [49], for each NOCS coordinate, we predict logits over non-overlapping bins, and use the softmax to obtain the final NOCS value. Our ablations show that this choice works best on our challenging OmniNOCS dataset.
+
+Size and learned PnP heads: We use a size head to predict the 3D size to scale the NOCS predictions to metric object coordinates. Our size head also uses self-attention blocks. Contrary to previous work [7,28,49], it does not use any class-specific layers or per-class average size statistics, as these limit scalability to larger and diverse vocabularies. Our learned PnP head is inspired from [32,48] and uses convolutional layers to predict the 3D orientation and a 2D projection of the 3D centroid. The orientation is predicted using the 6D partial rotation matrix representation [57] in allocentric space [7,28].
+
+# 4.2 Localization from NOCS predictions
+
+To localize the object in 3D, we first scale the predicted NOCS using the predicted size to obtain unnormalized metric object coordinates. NOCSformer's learned PnP head predicts the object orientation in allocentric space, which is converted to the egocentric frame using the predicted projection of the 3D centroid. Using the unnormalized 3D object coordinates, object orientation, and the 3D centroid ray, the object depth is estimated from the corresponding 2D points.
+
+Alternatively, the 6DoF pose for the object can be solved directly (without using the learned PnP head) by solving a PnP problem using efficient solvers [30]. However, the orientations so obtained are known to be more sensitive to errors in NOCS coordinates, and less robust when compared to learned methods [32, 48].
+
+# 4.3 Losses
+
+When training NOCSformer, we supervise the NOCS, mask, 3D size, orientation and centroid predictions using appropriate losses. We supervise predicted masks using a simple L2 loss with respect to the ground truth instance mask. Since our NOCS head uses binned prediction where the final NOCS value is a softmax over non-overlapping bin logits, we use a combination of cross-entropy and regression losses.
+
+$$
+\begin{array}{l} \mathcal {L} _ {\text {m a s k}} = \left\| \operatorname {m a s k} _ {\text {p r e d}} - \operatorname {m a s k} _ {\text {g t}} \right\| _ {2} \\ \mathcal {L} _ {N O C S} = \operatorname {s o f t m a x} \mathrm {C E} (\hat {\mathbf {n}}, \hat {\mathbf {n}} _ {g t}) + | | \mathbf {n} - \mathbf {n} _ {g t} | | _ {1} \\ \end{array}
+$$
+
+where  $\hat{\mathbf{n}}$  are the predicted logits over the discretized bins and  $\mathbf{n}$  the continuous NOCS coordinate prediction.
+
+In addition to the supervised loss above, we also use a variant of the self-supervised NOCS loss  $\mathcal{L}_{ss}$  from [11] that minimizes the reprojection error of the predicted NOCS using the ground truth pose and predicted mask. More details about  $\mathcal{L}_{ss}$  are in the supplementary material. The NOCS and mask losses are applied to the fixed resolution grid predictions. Since our NOCS ground truth can be sparse, it is applied at only those locations on the grid that have valid NOCS ground truth.
+
+Since our 3D size head also uses binned prediction, we use a combination of softmax cross-entropy and L1 loss for supervision. Note that our size loss is normalized by the ground truth size, in order to penalize errors on smaller objects equally. For the learned PnP head, we also supervise the centroid and rotation predictions using L1 losses in their output space.
+
+$$
+\begin{array}{l} \mathcal {L} _ {\text {s i z e}} = \operatorname {s o f t m a x C E} (\hat {\mathbf {s}}, \hat {\mathbf {s}} _ {g t}) + | \mathbf {s} - \mathbf {s} _ {\mathbf {g} t} | / \mathbf {s} _ {\mathbf {g} t} \\ \mathcal {L} _ {r o t} = \left\| ^ {c} \mathbf {R} _ {o _ {p r e d}} - ^ {c} \mathbf {R} _ {o _ {g t}} \right\| _ {1, 1} \\ \mathcal {L} _ {\text {c e n t r o i d}} = \left\| \mathbf {c} _ {o} - \mathbf {c} _ {o g t} \right\| _ {2} \\ \mathcal {L} _ {P n P} = \mathcal {L} _ {r o t} + \mathcal {L} _ {c e n t r o i d} \\ \end{array}
+$$
+
+The total loss for training NOCSformer is a weighted sum:
+
+$$
+\mathcal {L} _ {\text {t o t a l}} = w _ {\text {s i z e}} \mathcal {L} _ {\text {s i z e}} + w _ {\text {m a s k}} \mathcal {L} _ {\text {m a s k}} + w _ {\text {N O C S}} \mathcal {L} _ {\text {N O C S}} + w _ {s s} \mathcal {L} _ {s s} + w _ {P n P} \mathcal {L} _ {P n P}
+$$
+
+# 5 Experiments
+
+Although previous works predict NOCS accurately on a few categories [32, 48, 49], they only evaluate on 3D detection (localization) or pose estimation tasks, without quantifying the accuracy of the predicted NOCS. In section 5.1, we propose metrics and establish a benchmark to evaluate NOCS on the OmniNOCS dataset. We compare the localization accuracy of NOCSformer against existing benchmarks, by evaluating its localization accuracy on nuScenes [10] in section 5.2. We also evaluate the unique ability of NOCSformer to transfer to unseen datasets and domains. Finally, we provide ablations on critical design choices made in our model in section 5.4.
+
+![](images/2aa0ca2f97ae637fa30545efd1f1ae8588bb1f4cb61de62055b8d308fd639897.jpg)  
+Input: Image + 2D box
+
+![](images/7b301e035cb9a8d792ee47cfe0d9599f6706dbcc92612ef7f286c08862e669ac.jpg)
+
+![](images/374cb9ec2555543a715f6dc158b5c9f71b8bf0a1361435068508944cbd3e1f21.jpg)
+
+![](images/c90f8e2d618abcd867d7ddadf1c909e5f57fd75cc105dc447ac07813344f5228.jpg)
+
+![](images/96c2e128ca476ddc8a22a180ed59a9c443c6f8d4ea3609bb8c624b657e935d26.jpg)
+
+![](images/a75054222dc8a2216e3c6c7b26086911937082e67b44a8ebffcdc7467c9d1cd2.jpg)  
+Input
+
+![](images/0ef75d46db01758059058fdc5084c36ae8f7da4761f8b7e7b386cd2c7d3f1770.jpg)  
+Output
+
+![](images/7c4c4a5913f8a345de088583bbb8d42af25a19035a8a360d0213197d5bb428f5.jpg)  
+Output: NOCS + 3D box
+
+![](images/7fe99a872f668d3e02d09b6a612fa8fa521921e1e8846569e8b5053137341184.jpg)
+
+![](images/a7d3b1bc3efc9aa5aff2ea352c5a4e219af6004624e4599109ad87551ed346fe.jpg)
+
+![](images/d1e6c396d4789631aed34fd84b72253a13bbc7c4726a8b8a8d149548ebd6d902.jpg)
+
+![](images/c93c50b71f3a0679cbee0d98cd5ddb9b7d40baa192daae55e87c6277130e90f3.jpg)
+
+![](images/edaef087919be95f65a5f8c5ffbe06550f0d08f4af2413dd3ce1bfa071c9615f.jpg)  
+3D shape + pose
+
+![](images/135fba02399742b52f7bf8d47d37cdc60ac04fdf5c906eeef00a8a3e17f2c426.jpg)  
+Input  
+Fig. 4: Example results of our single unified NOCSformer model across various datasets and object classes. The left column shows input images and query 2D bounding boxes. The center column shows the NOCS+instance maps predicted by NOCSformer along with the estimated 3D pose overlaid on the input image. The NOCS can be used with the 3D boxes and object size to lift the objects to a 3D pointcloud, which is shown in the right column. Last row contains two examples.
+
+![](images/3a6dd6f3997c50cc674624ac3f3d733e29435e73516117f81e5c2b75dd73fed8.jpg)  
+Output: 3D shapes and poses
+
+![](images/097c300d06565cea7c36582b922499faea658fd528ff385a152f90fca82cbed5.jpg)
+
+![](images/28e41a371836d1d79a9b4750352f22b011c92bc830eb73c3691e3ae75ee379db.jpg)
+
+![](images/1cbaa09de46cdf437354ebc4676ed9502833e92d77b062bdf21cac896a6c73b9.jpg)
+
+![](images/22cc7db8e1952ec5bf580d17ccce4c8628cc0343d2d6594597231b55fd5e1863.jpg)  
+Output  
+3D shape + pose
+
+![](images/7e24f158250b514172e978841e99570edd2f3e7d1ccdce649cdf5224a056b9d3.jpg)
+
+Datasets: For our experiments, we train NOCSformer on the OmniNOCS dataset containing 97 classes, holding out images from the NOCS-Real275 dataset. We use NOCS-Real275 to evaluate NOCSformer's cross-dataset generalization capabilities by not training on it. NOCS-Real275 has 6 classes that overlap with the rest of OmniNOCS, although they are not the top 20 classes. It also differs from the rest of OmniNOCS in terms of camera parameters and the context of objects in the scene.
+
+# 5.1 NOCS prediction accuracy
+
+We propose the use of NOCS mAE and NOCS mPSNR to evaluate the accuracy of predicted NOCS. For each object the mean Absolute Error (mAE) and mPSNR are computed for all points in the intersection of ground truth and predicted masks, and reported as a mean of per-category means. However, the NOCS mAE/mPSNR do not penalize undersegmentations / sparse predictions. Since we also would like the NOCS predictions to span the full visible instance, we also evaluate the 2D mask mIoU.
+
+We evaluate NOCSformer on a subset of OmniNOCS that contains 75 classes with accurate ground truth NOCS and masks, see Table 3. It is able to predict NOCS with errors less than  $9\%$  (0.089 mAE on OmniNOCS). We also evaluate on the held-out NOCS-Real275 dataset, which was not used to train NOCSformer. We find that the zero-shot NOCSformer outperforms the NOCS baseline from [49] that was fully trained on this dataset, a strong indication of the generalization capabilities of NOCSformer.
+
+# 5.2 3D localization accuracy
+
+NOCS provides dense 3D-2D correspondences that can be used to estimate the 3D object oriented bounding box. This is done by using the predicted object size and solving a PnP problem, as explained in Sec. 4.2. We evaluate the accuracy of our estimated bounding boxes in different settings:
+
+Outdoor scenes: We compare NOCSformer's 3D localization accuracy to that of other 3D detection models using the nuScenes true positive localization metrics on the challenging nuScenes dataset (included in OmniNOCS). We compare to Cube R-CNN, since it is the only other model that generalizes across diverse datasets. However, Cube R-CNN has two notable differences to NOCSformer: 1) being a detection model, it also jointly detects 2D object regions, and 2) it uses Chamfer loss causing it to have high orientation errors. As a more comparable baseline, we use a variant of our model (termed "Cubeformer"), by replacing our NOCS head with the cube head from Cube R-CNN
+
+<table><tr><td rowspan="2">Method</td><td colspan="3">NOCS-Real275</td><td colspan="3">OmniNOCS</td></tr><tr><td>NOCS MAE↓</td><td>NOCS PSNR↑</td><td>Mask IoU↑</td><td>NOCS MAE↓</td><td>NOCS PSNR↑</td><td>Mask IoU↑</td></tr><tr><td>NOCS baseline [49]</td><td>0.121</td><td>16.345</td><td>86.10</td><td>-</td><td>-</td><td>-</td></tr><tr><td>NOCSformer</td><td>0.107</td><td>18.527</td><td>89.03</td><td>0.094</td><td>20.245</td><td>78.50</td></tr></table>
+
+Table 3: NOCS and mask prediction evaluation: We report metrics for NOCSformer on 75 classes of OmniNOCS: it is the first model that is capable on predicting NOCS on such diverse data. Additionally, NOCSformer outperforms [49] which trained on NOCS-Real275, without training on NOCS-Real275.
+
+<table><tr><td>Method</td><td>Multi-dataset</td><td>mATE (m)↓</td><td>mAOE (rad)↓</td><td>mASE (%)↓</td><td>mIoU↑</td></tr><tr><td>FCOS3D [51]</td><td>X</td><td>0.777</td><td>0.400</td><td>0.231</td><td>-</td></tr><tr><td>PGD [50]</td><td>X</td><td>0.675</td><td>0.399</td><td>0.236</td><td>-</td></tr><tr><td>EProPNP [12]</td><td>X</td><td>0.676</td><td>0.363</td><td>0.263</td><td>-</td></tr><tr><td>EProPNP + TTA [12]</td><td>X</td><td>0.653</td><td>0.319</td><td>0.255</td><td>-</td></tr><tr><td>Cube R-CNN [7]</td><td>✓</td><td>0.650</td><td>1.305</td><td>0.283</td><td>0.349</td></tr><tr><td>Cubeformer</td><td>✓</td><td>0.790</td><td>0.573</td><td>0.301</td><td>0.280</td></tr><tr><td>NOCSformer</td><td>✓</td><td>0.887</td><td>0.558</td><td>0.291</td><td>0.377</td></tr></table>
+
+without a Chamfer loss. It uses the input 2D boxes. More details about Cubeformer are in the supplementary material. From Table 4, we find that NOCSformer's box orientations are canonical and more accurate than both Cube R-CNN and Cubeformer. Moreover, the boxes estimated from NOCSformer's NOCS predictions are comparable in translation and scale errors to those of Cube R-CNN and Cubeformer, even though it does not directly regress depth. While all 3 methods generalize across several classes and domains, baselines trained solely on nuScenes localization (top half) significantly outperform them on this task.
+
+Cross-dataset generalization (indoor): Here, we hold out NOCS-Real275 from OmniNOCS when training NOCSformer, and use it to evaluate NOCSformer's ability to generalize to unseen domains. NOCS-Real275 features a tabletop setting with multiple objects, which is semantically different from our other indoor datasets. We compare against Cube R-CNN [7] and the NOCS [49] model that was trained from scratch on this dataset alone. [49] also uses class-specific model parameters and additional losses for symmetric objects that do not scale to larger vocabularies.
+
+The results are shown in Table 5. We find that NOCSformer is more accurate at transferring to this unseen dataset compared to Cube R-CNN, indicating the generalizability of NOCS-based localization methods over that of box-regression methods. Note that the mAP metrics used in the NOCS-Real275 datasets may also be affected by the false positives/negatives from Cube R-CNN. However, both models are worse than the supervised NOCS baseline.
+
+Table 4: Outdoor localization: Comparison of 3D localization accuracy on the nuScenes subset of OmniNOCS. Top half shows methods that are only trained on nuScenes: while these perform better on nuScenes itself, they do not generalize to other datasets and classes. OmniNOCS is competitive with Cube R-CNN on mIoU while also predicting canonical orientations and object coordinates. Note that mIoU and orientation error only uses the predicted yaw orientation component.  
+
+<table><tr><td rowspan="2">Method</td><td rowspan="2">Transferred?</td><td rowspan="2">Depth input?</td><td colspan="2">mAP</td></tr><tr><td>3D IoU @ 25</td><td>3D IoU @ 50</td></tr><tr><td>NOCS model [49]</td><td>X</td><td>✓</td><td>79.6</td><td>72.4</td></tr><tr><td>CubeRCNN [7]</td><td>✓</td><td>X</td><td>14.9</td><td>4.1</td></tr><tr><td>NOCSformer</td><td>✓</td><td>X</td><td>43.5</td><td>10.6</td></tr></table>
+
+Table 5: Cross-dataset generalization: Comparison of localization accuracy on NOCS-Real275 for all classes, using mAP at different thresholds. This dataset has been held out when training CubeRCNN and NOCSformer. NOCSformer is able to generalize to NOCS-Real275 without any additional training.
+
+![](images/7c23736e5a82817d3573db53649829c2d26c417528908f20903bc18b98a89633.jpg)  
+Fig. 5: Generalization across datasets: NOCSformer can generalize to new datasets that present new camera models and object domains. We show this zero-shot capability of NOCSformer (bottom row) on the NOCS-Real275 test set without training on NOCS-Real275 dataset. The predictions from a NOCS baseline [49] trained explicitly on the NOCS-Real275 dataset are shown in the top row.
+
+# 5.3 3D orientation accuracy
+
+A key challenge for 3D localization models trained on multiple datasets [7] is predicting canonical object orientations, as these datasets typically differ in their object pose conventions. OmniNOCS ensures that object poses in all constituent datasets are consistent within a category, enabling our model to predict canonical orientations. We evaluate this by computing the accuracy at different orientation error threshold on KITTI-val [13] in Table 6. We compare the accuracy of our orientation predictions to those of Cube R-CNN [7], which are trained using Chamfer loss, and therefore suffers at predicting canonical orientations. We also compare against our Cubeformer baseline that directly supervises the orientations. We find that orientations estimated using NOCSformer are more consistent and accurate than both our baselines.
+
+<table><tr><td rowspan="2">Method</td><td colspan="3">Gravity axis</td><td colspan="3">Heading axis (X)</td></tr><tr><td>Accuracy @ 1 deg</td><td>Accuracy @ 5 deg</td><td>Accuracy @ 90 deg</td><td>Accuracy 5 deg</td><td>Accuracy @ 10 deg</td><td>Accuracy @ 90 deg</td></tr><tr><td>CubeRCNN</td><td>5.75</td><td>18.29</td><td>51.74</td><td>19.43</td><td>21.55</td><td>34.17</td></tr><tr><td>Cubeformer</td><td>80.79</td><td>98.52</td><td>100.0</td><td>41.99</td><td>55.88</td><td>77.26</td></tr><tr><td>NOCSformer</td><td>84.24</td><td>99.95</td><td>100.0</td><td>49.16</td><td>59.65</td><td>81.99</td></tr></table>
+
+Table 6: 3D orientation accuracy for models trained on multiple datasets: Models that use Chamfer distance (CubeRCNN) for supervising orientation heads end up being inconsistent in their orientation predictions. The results are averaged over 5 classes in the KITTI-val subset [13].
+
+<table><tr><td>Architecture</td><td>NOCS PSNR↑</td><td>Mask IoU↑</td></tr><tr><td>NOCSformer</td><td>20.431</td><td>81.69</td></tr><tr><td>Hourglass head</td><td>-3.064</td><td>-3.16</td></tr><tr><td>MaskRCNN head</td><td>-5.23</td><td>-21.27</td></tr><tr><td>w/o discretized prediction</td><td>-4.11</td><td>-</td></tr></table>
+
+Table 7: Comparison of different architecture choices for the NOCS and mask prediction head: We experiment with transformer, hourglass and MaskRCNN architectures. We compare whether discretized prediction of NOCS is better than continuous regression for transformer heads. We quantify the difference in performance when using separate heads for mask and NOCS prediction as opposed to a single head.
+
+# 5.4 Ablations
+
+NOCS head architecture: While all existing architectures use a few CNN layers for their NOCS heads [49], we find that this significantly limits the NOCS prediction performance when scaling to more classes. Using our transformer NOCS head provides a 5.23dB improvement in NOCS PSNR and a  $21.27\%$  improvement in mask mIoU. We also experiment with a larger convolutional head: the Hourglass model from [6]. We find that the NOCSformer NOCS heads are even better than Hourglass, with an improvement of 3.06dB on NOCS PSNR and  $3.16\%$  on mask IoU (as shown in Table 7).
+
+Discretized versus continuous prediction of NOCS: NOCSformer models the final NOCS prediction as a classification layer with 50 non-overlapping bins. This was observed to be better than a linear regression layer with a MaskRCNN head in [49]. We find that it is also significantly better when using a transformer head, improving the NOCS prediction PSNR by 4.11 dB, as in Table 7.
+
+# 6 Conclusion and Future work
+
+This paper has introduced a new large scale dataset of Normalized Object Coordinates (NOCS) for a wide variety of object classes in indoor and outdoor scenes. It has also proposed a single transformer-based model NOCSformer that can predict object 3D pose, size, and NOCS for any of these objects given 2D bounding box inputs. These allows our model to obtain 3D shape and oriented bounding boxes of objects in metric scale from a single input image. This represents the first attempt to generalize NOCS estimation beyond small datasets of narrow domain, increasing the number of object categories available by an order of magnitude. We hope this provides a means for others to explore large-scale monocular 3D object pose and shape estimation.
+
+Some limitations of our current method include the handling of symmetric objects, where the coordinate system has multiple possible solutions, and reflected geometry, such as left / right shoes. Future work could address these issues, for example, minimizing over multiple coordinate frames in the loss for symmetric objects, and potential estimation of left / right coordinate frames for reflected geometry.
+
+# References
+
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+51. Wang, T., Zhu, X., Pang, J., Lin, D.: Fcos3d: Fully convolutional one-stage monocular 3d object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 913-922 (2021)  
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+
+53. Wen, B., Lian, W., Bekris, K., Schaal, S.: Catgrasp: Learning category-level task-relevant grasping in clutter from simulation. ICRA 2022 (2022)  
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2024/OmniNOCS_ A unified NOCS dataset and model for 3D lifting of 2D objects/images.zip

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2024/OmniSSR_ Zero-shot Omnidirectional Image Super-Resolution using Stable Diffusion Model/7452c846-9323-4781-952d-a6062713a9d5_origin.pdf

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2024/OmniSSR_ Zero-shot Omnidirectional Image Super-Resolution using Stable Diffusion Model/full.md

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+# OmniSSR: Zero-shot Omnidirectional Image Super-Resolution using Stable Diffusion Model
+
+Runyi Li†, Xuhan Sheng†, Weiqi Li, and Jian Zhang
+
+School of Electronic and Computer Engineering, Peking University, China {lirunyi, shengxuhan, liweiqi}@stu.pku.edu.cn {zhangjian.sz}@pku.edu.cn
+
+Abstract. Omnidirectional images (ODIs) are commonly used in real-world visual tasks, and high-resolution ODIs help improve the performance of related visual tasks. Most existing super-resolution methods for ODIs use end-to-end learning strategies, resulting in inferior realness of generated images and a lack of effective out-of-domain generalization capabilities in training methods. Image generation methods represented by diffusion model provide strong priors for visual tasks and have been proven to be effectively applied to image restoration tasks. Leveraging the image priors of the Stable Diffusion (SD) model, we achieve omnidirectional image Super Resolution with both fidelity and realness, dubbed as OmniSSR. Firstly, we transform the equirectangular projection (ERP) images into tangent projection (TP) images, whose distribution approximates the planar image domain. Then, we use SD to iteratively sample initial high-resolution results. At each denoising iteration, we further correct and update the initial results using the proposed Octadecaplex Tangent Information Interaction (OTII) and Gradient Decomposition (GD) technique to ensure better consistency. Finally, the TP images are transformed back to obtain the final high-resolution results. Our method is zero-shot, requiring no training or fine-tuning. Experiments of our method on two benchmark datasets demonstrate the effectiveness of our proposed method.
+
+Keywords: Omnidirectional Imaging  $\cdot$  Super-Resolution  $\cdot$  Latent Diffusion Model
+
+# 1 Introduction
+
+Omnidirectional images (ODIs) capture the entire scene in all directions, exceeding the narrow field of view (FOV) offered by planar images. Super-Resolution
+
+(SR) techniques enhance the visual quality of ODIs by increasing their resolution, thereby revealing finer details and enabling more accurate scene analysis and interpretation. This becomes particularly crucial in applications like virtual reality and surveillance, where high-resolution ODIs are essential for precise perception and decision-making.
+
+Current research in omnidirectional image super-resolution (ODISR) explores various methodologies to enhance the resolution of ODIs [14,36]. SphereSR [59] addresses non-uniformity in different projections by learning upsampling processes and ensuring information consistency using LIIF [5]. OSRT [60] designs a distortion-aware Transformer to modulate equirectangular projection (ERP) distortions continuously and self-adaptively. Without a cumbersome process, OSRT outperforms previous methods remarkably. However, existing ODISR methods face the following challenges: (1) The majority are end-to-end models that can only produce a deterministic output, always better data fidelity but worse visual perception quality [17]. It's promising to develop a generation-based model, but requiring high data demands, yet high-resolution ODIs are high cost to collect [54,55]. (2) Most methods directly perform SR on ERP format ODIs, while users usually watch ODIs in a narrow FOV using tangent projection (TP). So another promising direction is to use off-the-shelf planar models on TP images. Recent times have witnessed the introduction and widespread application of diffusion models [23,43], especially Stable Diffusion (SD) [38], which have provided a robust backbone for visual tasks [21,24,56,61], including SR [30,40,47,51,52,62]. However, if TP images are trivially one-by-one processed using diffusion-based SR models, they will exhibit discrepancies in the overlapping region when reprojected onto the ERP image. As a result, the global continuity is compromised.
+
+Leveraging the strong image prior provided by SD, we propose the first diffusion-based zero-shot method for ODISR, named OmniSSR. Specifically, we propose Octadecaplex Tangent Information Interaction (OTII). OTII entails iterative conversion of intermediate SR results between ERP and TP, bridging the domain gap between ODIs and planar images. Building upon OTII, we further employ an approximate analytical solution of gradient descent, namely as Gradient Decomposition, to guide high-fidelity, high-quality omnidirectional image SR. By capitalizing on SD's effective image prior, our approach strikes a balance between fidelity and realness, ensuring that the restored ODIs exhibit both fidelity to the input data and realistic visual details. This method shows potential for advancing the current state of ODISR, providing improved resolution and visual quality across various applications. Fig. 1 showcases results fully demonstrating the superiority and performance of our proposed methods.
+
+Our main contributions are summarized as follows:
+
+- We propose OmniSSR, the first zero-shot ODISR method, using an off-the-shelf diffusion-based model, requiring no training or fine-tuning, leveraging existing image generation model priors to solve ODISR task.  
+- To bridge the domain gap between ODIs and planar images, we introduce Octadecaplex Tangent Information Interaction by repeatedly transforming
+
+![](images/6ac71c55133d5a4b08665715de6b12acbba8f34c2673ad66ddd81cf56ad040db.jpg)  
+(a) Input low-resolution ERP image
+
+![](images/190d9e5c51da8136830c6232a827c9aa1efaba08f16c4afe093ba374594cf240.jpg)  
+Degraded tangent image
+
+![](images/879565a0658a71bfcbda8b4f2d3b2bc3e444063d1c1fa0ab43f0587f20de7526.jpg)  
+Restored by OSRT
+
+![](images/aa214b3782fdb33ef8a9ee5f1a9ea71fae5259094da3e7cdfd81f20ffd36e2d5.jpg)  
+Restored by OmniSSR
+
+![](images/92878b1636165f5382bb60c5bd106c1975f4a9828b3623e262c61e3c2b49d295.jpg)  
+Ground truth
+
+![](images/aaa341522c4a9f9e98e480a239146ccd5e106e49683f721c8356fdf458d422fa.jpg)  
+(b) Input low-resolution ERP image
+
+![](images/0dd37afa508f60ab7420e295835f7c76128acc59f5e704052d4493526b2ceb94.jpg)  
+Degraded tangent image
+
+![](images/9f30918bea3777a60968a73c3de20b972eed6387a7cfbd63b062475e81a346fe.jpg)  
+Restored by OSRT  
+Fig. 1: We address omnidirectional image super-resolution in a zero-shot manner via OmniSSR. Presented above are select outcomes that sketch the essence of OmniSSR compared with current state-of-the-art approach OSRT [60]. Part (a) and (b) illustrate that OmniSSR upholds fidelity and visual realness at the same time, providing vivid and realistic details, while OSRT outputs over-smoothed and distorted results. Zoom in for more details.
+
+![](images/1658a722c4f092baf6dec82180441937b062474937d82f77bc9b31dbb8ed3ecf.jpg)  
+Restored by OmniSSR
+
+![](images/4d010a53b7a099e9c3a72d109fd332f0e962bc3679bd46ce293cb2f67d46c5cc.jpg)  
+Ground truth
+
+ODIs between ERP format and TP format, enabling ODISR task with pretrained diffusion models on planar images.
+
+- By iteratively updating images using the developed Gradient Decomposition technique, we introduce consistency constraints into the sampling process of the latent diffusion model, ensuring a trade-off between fidelity and realness in the reconstructed results.  
+- Extensive experiments are conducted on the benchmark datasets, demonstrating the superior performance of our method over existing state-of-the-art approaches, which validate the effectiveness of OmniSSR.
+
+# 2 Related Work
+
+# 2.1 Single Image Super-Resolution (SISR)
+
+Image super-resolution methods based on deep learning have undergone significant development over an extended period. Currently, they can be broadly classified into two categories of solutions. The first category involves end-to-end network training methods, which utilize image pairs consisting of low-resolution degraded images and high-resolution ground truth images for network training [6-8, 27, 31, 32, 63, 66]. The network architectures employed in this category include CNN [16], Transformers [46], etc. The second category employs image generation models as priors, such as GAN [20], diffusion models [23, 43], etc., where low-resolution images are used as conditions to generate high-resolution images. We will mainly introduce the methods using generative prior.
+
+Single Image SR using GAN prior In SR works utilizing GAN pri- ors [3,12,33,37,58], including real-world scenarios [8,49,50,65], pre-trained GAN
+
+networks are employed to transform image features into latent space, where the corresponding latent code for the high-resolution image is searched, ultimately yielding the reconstructed high-resolution result.
+
+Single Image SR using diffusion prior The diffusion model provides a powerful image prior, and the diffusion sampling process can generate highly realistic images. This strong prior distribution can be applied to various image restoration tasks, including super-resolution [9, 10, 19, 40, 42, 51]. Image-domain diffusion models directly provide prior distributions of image-domain data. DDNM [51] based on the mathematical method of Range-Null space Decomposition, iteratively refines content on the zero space, combining image prior content in the value domain to achieve image restoration. DDRM [25] uses SVD decomposition to obtain restoration results, which is similar to DDNM. DPS [9] transforms the image super-resolution problem into an optimization problem with consistency constraints, using gradient descent algorithms to guide the generation of image-domain diffusion models. GDP [19] further uses such gradient to update the degradation operator to tackle blind image inverse problems. Other methods including MCG [10], DDS [9] and unified control of diffusion generation [19, 42] use same strategy for image restoration, especially image super-resolution.
+
+# 2.2 Omnidirectional Image Super-Resolution
+
+Omnidirectional image super-resolution (ODISR) aims to enhance the resolution of omnidirectional or 360-degree images, which are commonly captured by cameras with a wide field of view. This field has garnered increasing attention due to its applications in virtual reality, omnidirectional video streaming, and surveillance. Several approaches have been proposed to address the unique challenges of ODISR [1, 2, 35, 44]. For instance, Kämäräinen et al. [18] propose a deep learning-based approach for omnidirectional super-resolution, leveraging convolutional neural networks to effectively upscale low-resolution omnidirectional images while preserving spatial details. Similarly, Smolic et al. [36] introduce a novel omnidirectional super-resolution algorithm utilizing generative adversarial networks (GANs) to enhance the visual quality of omnidirectional images by hallucinating high-frequency details.
+
+For evaluation purposes, researchers commonly utilize datasets such as the ODI-SR dataset from LAU-Net [13], and SUN 360 Panorama dataset [53]. These datasets enable the quantitative assessment of ODISR algorithms across various scenarios and facilitate fair comparisons between different methods.
+
+# 3 Method
+
+In this section, we first briefly introduce the preliminary background of our method (Sec. 3.1), and give an overall view of our proposed OmniSSR (Sec. 3.2). Then, we discuss the designs of Octadecaplex Tangent Information Interaction, which transform ODIs between ERP and TP formats with pre-upsampling strategy (Sec 3.3), and the Gradient Decomposition correction (Sec. 3.4).
+
+# 3.1 Preliminaries
+
+$\mathrm{ERP} \leftrightarrow \mathrm{TP}$  Transformation The essence of projection transformations between ERP and TP lie in determining the positions of target image pixels within the source image and computing their corresponding pixel values using interpolation algorithms, as digital images are always stored discretely [28]. Therefore, the  $\mathrm{ERP} \rightarrow \mathrm{TP}$  transformation involves locating the TP image pixels on the ERP imaging plane, and vice versa. Gnomonic projection [11] provides the correspondence between ERP image pixels and TP image pixels.
+
+For a pixel  $P_{e}(x_{e},y_{e})$  within the ERP image, we first find its corresponding pixel  $P_{s}(\theta ,\phi)$  on the unit sphere using Eq. 1:
+
+$$
+\theta = 2 \pi x _ {e} / W, \phi = \pi y _ {e} / H, \tag {1}
+$$
+
+where  $H$  and  $W$  are the height and width of the ERP image. The Cartesian coordinates of the ERP image and the angular coordinates on the unit sphere exhibit a straightforward one-to-one linear relationship, suggesting a conceptual equivalence between these two projection formats.
+
+Given the spherical coordinates of the tangent plane center  $(\theta_c, \phi_c)$ , The transformation from  $P_s(\theta, \phi)$  to  $P_t(x_t, y_t)$ , i.e.  $\mathrm{ERP} \to \mathrm{TP}$ , is defined as:
+
+$$
+\begin{array}{l} x _ {t} = \left(\cos (\phi) \sin \left(\theta - \theta_ {c}\right)\right) / \zeta , \tag {2} \\ y _ {t} = \left(\cos \left(\phi_ {c}\right) \sin (\phi) - \sin \left(\phi_ {c}\right) \cos (\phi) \cos \left(\theta - \theta_ {c}\right)\right) / \zeta , \\ \end{array}
+$$
+
+where  $\zeta = \sin (\phi_c)\sin (\phi) + \cos (\phi_c)\cos (\phi)\cos (\theta -\theta_c).$
+
+The corresponding inverse transformation, i.e.  $\mathrm{TP} \rightarrow \mathrm{ERP}$ , is:
+
+$$
+\begin{array}{l} \theta = \theta_ {c} + \arctan \left(\left(x _ {t} \sin (c)\right) / \left(\rho \cos \left(\phi_ {1}\right) \cos (c) - y _ {t} \sin \left(\phi_ {c}\right) \sin (c)\right)\right), \\ \phi = \arcsin \left(\cos (c) \sin \left(\phi_ {c}\right) + y _ {t} \sin (c) \cos \left(\phi_ {c}\right) / \rho\right), \tag {3} \\ \end{array}
+$$
+
+where  $\rho = \sqrt{x_t^2 + y_t^2}$  and  $c = \arctan (\rho)$ .
+
+With Eq. 2 and Eq. 3, we can build one-to-one forward and inverse mapping functions between pixels on the ERP image and pixels on the TP images. An illustration of the  $\mathrm{ERP} \to \mathrm{TP}$  transformation is shown in Fig. 2(a).
+
+Iterative Denoising for Super-Resolution Utilizing the rich image priors provided by SD, we can super-resolve planar images. During initialization, the images are passed through the encoder  $\mathcal{E}$  of SD to obtain latent codes, which are then added to pure noise to generate initial noise  $\mathbf{z}_T$ . Following the approach proposed by StableSR [47], we pass the images through a time-aware adapter  $\mathcal{T}$ . This adapter network structure is similar to the down-sampling part in denoising UNet, taking the image and the time step  $t$  of diffusion sampling as inputs to obtain the latent code feature for step  $t$ . This feature, along with the latent code  $\mathbf{z}_t$  for each step and the time step  $t$ , is then passed through denoising UNet to calculate the denoised result  $\mathbf{z}_{0|t}$  and the latent code  $\mathbf{z}_{t - 1}$  for the next sampling step. By iterating this process  $T$  times, we can obtain the final super-resolution result via decoder  $\mathcal{D}$  of SD, yielding high-resolution images.
+
+![](images/a4fd2290aad0f8599fc02ad333da083137fca0b2a1c9da880711a869ad92e8a6.jpg)  
+(a) Equirectangular Projection  $\rightarrow$  Tangent Projection
+
+![](images/54e8b5067afbd7f4152bc0a06645d53a3b3ac2783204893c1eb94d54f11121e7.jpg)  
+(b) Pre-Upsampling Strategy  
+Fig. 2: Details about gnomonic transformations. (a) conversion from ERP to TP. (b) pre-upsampling proposed in Octadecaplex Tangent Information Interaction (Sec. 3.3) mitigating loss during transformation.
+
+# 3.2 Overview
+
+![](images/786d8ad01e6419d6a5b472aa6e445112d61640237c5fccdbbc4090fa2f2ffabb.jpg)  
+(b) Detailed illustration of GD Correction  
+Fig. 3: Overview of our proposed OmniSSR. Input low-resolution omnidirectional image  $\mathbf{E}_{init}$  in ERP format is first projected onto Tangent Projection (TP) images  $\mathbf{x}_{init}^{(1)},\mathbf{x}_{init}^{(2)},\dots,\mathbf{x}_{init}^{(m)}$ , then iteratively refined via Stable Diffusion (SD) with a time-aware adapter and controllable feature wrapping (CFW) module. In each step of diffusion sampling, we adopt the Gradient Decomposition (GD) correction technique to introduce consistency constraints for the restored intermediate results. After  $T$  steps of sampling, we obtain the final result  $\tilde{\mathbf{E}}_0$  with high resolution and better visual quality.
+
+Our approach can be divided into three parts. The first part is pre-processing, where we initially up-sample the low-resolution ERP images  $\mathbf{E}_{init}$  with bicubic interpolation to target high-resolution size, then project them onto tangent planes to obtain a series of TP images. These TP images are transformed to the latent space by the SD encoder, iteratively processed through denoising UNet and time-aware adapter network, and then decoded to obtain high-resolution TP images. During each denoising step, these TP images are transformed back via inverse transformation to ERP images, employing the Gradient Decomposition correction to ensure consistency constraints in diffusion sampling. After  $T$
+
+<table><tr><td colspan="2">Algorithm 1: OmniSSR Pipeline</td><td>Algorithm 2: Iterative Denois-ing with GD Correction</td></tr><tr><td colspan="2">Input: Einit, F, F-1, A, A†, E, D, T</td><td>Input: Einit, F, F-1, A, A†, E, D, T, Output: Latent code {z(1), z(2), ..., z(m)}</td></tr><tr><td colspan="2">Output: SR result E0</td><td>for t = T to 1 do</td></tr><tr><td>1</td><td>{x(1), x(2), ..., x(m)} = F(Einit)</td><td>for i = 1 to m do</td></tr><tr><td>2</td><td>for i = 1 to m do</td><td>εt = εθ(z(t), T(z(i), t), t)</td></tr><tr><td>3</td><td>z(i)init = Ε(x(i))</td><td>z(0|t) = 1/√αt(z(t)i - εt√1-αt)</td></tr><tr><td>4</td><td>ε(i) ∼ N(0, I)</td><td>x(0|i) = D(z(i))</td></tr><tr><td>5</td><td>zT(i) = √αTz(i) + √1-αTε(i)</td><td>end</td></tr><tr><td>6</td><td>end</td><td>E0|t = F-1{x(1), x(2), ..., x(m)}</td></tr><tr><td>7</td><td>Get{z(0), z(2), ..., z(m)} from Algo. 2</td><td>E0|t = E0|t + γeA†(Einit - AE0|t)</td></tr><tr><td>8</td><td>for i = 1 to m do</td><td>{x(1), x(2), ..., x(m)} = F(E0|t)</td></tr><tr><td>9</td><td>x(0)i = D(z(0))</td><td>for i = 1 to m do</td></tr><tr><td>10</td><td>end</td><td>z(0|i) = (1-γl)z(0|i) + γlE(x(i))</td></tr><tr><td>11</td><td>E0 = F-1{x(0), x(2), ..., x(m)}</td><td>z(t-1) ∼ p(z(t-1)|z(t,i), x(0|i))</td></tr><tr><td>12</td><td>E0 = E0 + γpA†(Einit - AE0)</td><td>end</td></tr><tr><td>13</td><td>return E0</td><td></td></tr><tr><td>14</td><td>end</td><td></td></tr><tr><td>15</td><td>return {z(0), z(2), ..., z(m)}</td><td></td></tr></table>
+
+iterations, the final super-resolution result is obtained. A formulaic description for OmniSSR is shown in Algo. 1. Fig. 3 shows the overview of our pipeline.
+
+# 3.3 Octadecaplex Tangent Information Interaction (OTII)
+
+Motivation To apply SD for ODISR, a straightforward way is to perform the  $\mathrm{ERP} \rightarrow \mathrm{TP}$  transformation on the input ERP image. Then, each obtained TP image is fed into the SD-based model for SR. Finally, the  $\mathrm{TP} \rightarrow \mathrm{ERP}$  transformation yields the ultimate super-resolved ERP image. OmniFusion [28] employs a similar approach for depth estimation. However, this simplistic strategy fractures the inherent global coherence of ODIs, leading to pixel-level discontinuities in the fused ERP images. Moreover, interpolation algorithms cause significant information loss in the original projection transformations, resulting in more blurred images. If applied multiple times, this exacerbates the information loss even further. To mitigate this, a trivial solution is to increase the pixel count (resolution) of the intermediate projection imaging plane. However, excessively high resolutions in TP images can introduce unnecessary computational overhead during the denoising stage and potentially compromise the denoising performance. (see Supplementary Materials for details)
+
+Information Interaction and Pre-upsampling Based on the analysis of the Motivation in 3.3, we propose OTII by alternating the intermediate results between ERP and TP formats at each denoising step, where a single ERP image is represented by 18 TP images. From Sec. 3.1, we can achieve the  $\mathrm{ERP}\rightarrow \mathrm{TP}$  transformation (denoted as  $\mathcal{F}(\cdot)$ ) and the  $\mathrm{TP}\rightarrow \mathrm{ERP}$  transformation (denoted as  $\mathcal{F}^{-1}(\cdot)$ ). Through the  $\mathrm{ERP}\rightarrow \mathrm{TP}$  transformation, we can convert distorted ERP
+
+images into TP images with content distributions that approximate those of planar images. This enables the use of the original SD super-resolution method for planar images. Conversely, through the  $\mathrm{TP} \rightarrow \mathrm{ERP}$  transformation, we can fuse information between different TP images holistically, while providing ERP-format input for the subsequent GD Correction in Sec. 3.4. To handle information loss during projection transformation, we further propose to pre-upsample the source image before projection transformations, as shown in Fig. 2(b). Our experiments in Sec. 4.4 demonstrate that this pre-upsampling strategy can significantly mitigate the information loss caused by projection transformations.
+
+# 3.4 Gradient Decomposition (GD) Correction for Fidelity
+
+SD-based methods, as introduced in Sec. 3.1, can perform SR on sliced TP images. However, relying solely on the SR results from SD may lack consistency and fail to accurately preserve the original semantic information and details of the low-resolution image. To enhance the consistency of the SR results from SD, we opt to use convex optimization methods to iteratively refine them. Modeling the SR task as an image inverse problem, the following equation is formulated:
+
+$$
+\mathbf {y} = \mathbf {A x} + \mathbf {n}, \quad \mathbf {n} \sim \mathcal {N} (\mathbf {0}, \mathbf {I}), \tag {4}
+$$
+
+where  $\mathbf{x}$  represents the original image,  $\mathbf{y}$  denotes the degraded result,  $\mathbf{A}$  is the degradation operator (e.g., bicubic downsampling for super-resolution), and  $\mathbf{n}$  is random noise. The objective we aim to solve can be expressed as the following convex optimization problem:
+
+$$
+\underset {\mathbf {x}} {\operatorname {a r g m i n}} | | \mathbf {y} - \mathbf {A x} | | _ {2} ^ {2} + \lambda \mathcal {R} (\mathbf {x}), \tag {5}
+$$
+
+where the first term is the data-fidelity term, ensuring the consistency of image reconstruction, and the second term is the regulation term, ensuring the sparsity of the reconstruction result, thus making the image more realistic. The regularization term can be the 1-norm, Total Variation, etc. The aforementioned convex optimization problem can be solved using a series of algorithms, such as gradient descent, ADMM [4], etc. Considering the trade-off between time and performance, we turn to find a solution based on gradient descent, and provide an approximate analytical solution composed of a fidelity term and a realness term, named "Gradient Decomposition (GD)":
+
+$$
+\begin{array}{l} \tilde {\mathbf {E}} _ {0 | t} = \mathbf {E} _ {0 | t} + \alpha \nabla_ {\mathbf {E} _ {0 | t}} \left\| \mathbf {E} _ {i n i t} - \mathbf {A} \mathbf {E} _ {0 | t} \right\| _ {F} = \mathbf {E} _ {0 | t} + \alpha \times 2 \left(\mathbf {A} ^ {\dagger} \mathbf {E} _ {i n i t} - \mathbf {A} ^ {\dagger} \mathbf {A} \mathbf {E} _ {0 | t}\right) \\ = \mathbf {E} _ {0 | t} + \gamma \mathbf {A} ^ {\dagger} \left(\mathbf {E} _ {i n i t} - \mathbf {A} \mathbf {E} _ {0 | t}\right) = \gamma \mathbf {A} ^ {\dagger} \mathbf {E} _ {i n i t} + (\mathbf {I} - \gamma \mathbf {A} ^ {\dagger} \mathbf {A}) \mathbf {E} _ {0 | t} \tag {6} \\ \end{array}
+$$
+
+where  $\mathbf{A}^{\dagger}$  denotes pseudo-inverse of degradation operator  $\mathbf{A}$ ,  $\mathbf{E}_{init}$  denotes initial low-resolution ERP input,  $\mathbf{E}_{0|t}$  denotes restored result by SD,  $\tilde{\mathbf{E}}_{0|t}$  denotes corrected result by GD,  $\alpha$  denotes the learning rate of gradient descent, and  $\gamma$
+
+denotes the simplified hyper-parameter which is further tuned using grid search. The final setting of  $\gamma$  on different stages is shown in Sec. 4.1, and the ablation studies of parameter choice are in Sec. 4.4.
+
+This technique could be seen as a step of gradient descent optimization, and the optimized result could be decomposed of (1)  $\gamma \mathbf{A}^{\dagger}\mathbf{E}_{init}$ , which ensures the consistency of the generated result, and (2)  $(\mathbf{I} - \gamma \mathbf{A}^{\dagger}\mathbf{A})\mathbf{E}_{0|t}$ , which serves as the iteratively updated generated result to improve its realness;  $\gamma$  is a hyperparameter balancing the data fidelity and visual quality. For a better diversity and generality of the SR process, we expand this solution to latent space, and obtain the denoising result from both denoising UNet and corrected TP images (Algo. 2 line 11). A more detailed understanding of the iterative denoising process and application of GD correction could be referred to Algo. 2.
+
+# 4 Experiments
+
+# 4.1 Implementation Details
+
+Datasets and Pretrained Models We choose the test set of ODI-SR dataset from LAU-Net [13] and SUN 360 Panorama dataset [53], comprising 97 and 100 omnidirectional images respectively, for experimental evaluation. The ground truth images are of size  $1024 \times 2048$  pixels. In SR methods such as GDP [19] and PSLD [39] for planar images, we partitioned the images into several  $256 \times 256$  patches and performed super-resolution on each patch individually.
+
+For pre-trained models, we adopt from StableSR [47], which provided a SR network for planar images based on SD. This network architecture includes a time-aware adapter, a controllable feature wrapping (CFW) module, and the original SD structure from HuggingFace. All of them are kept untrained in our proposed OmniSSR.
+
+Settings We set diffusion sampling steps to 200, which is the same as StableSR. The steps for other diffusion-based methods are set the same as their default settings (e.g. 1000 steps for PSLD). The degradation for low-resolution ERP images is bicubic down-sampling, and the implementation of its pseudo-inverse can be referred from code of DDRM  $[25]^2$ . For choices of hyper-parameter  $\gamma$  in GD correction, we set  $\gamma_p = 1.0$ ,  $\gamma_e = 1.0$ ,  $\gamma_l = 0.5$ . Our code is developed via PyTorch on NVIDIA 3090Ti GPU.
+
+# 4.2 Comparison of OmniSSR with diffusion-based methods
+
+To evaluate the performance of proposed OmniSSR, we compare our method with recent state-of-the-art zero-shot methods for single image SR task: DPS [9], DDRM [25], GDP [19] which are based on the image-domain diffusion model,
+
+Table 1: SR results under bicubic downsampling on ODI-SR and SUN 360 Panorama datasets. For tasks not implemented in those papers, we mark N/A in the corresponding results. The best results are shown in bold, and the second best results are underlined.  
+
+<table><tr><td rowspan="2">Method</td><td rowspan="2">Scale</td><td colspan="4">ODI-SR</td><td colspan="4">SUN 360 Panorama</td></tr><tr><td>WS-PSNR↑</td><td>WS-SSIM↑</td><td>FID↓</td><td>LPIPS↓</td><td>WS-PSNR↑</td><td>WS-SSIM↑</td><td>FID↓</td><td>LPIPS↓</td></tr><tr><td>Bicubic</td><td></td><td>28.14</td><td>0.8343</td><td>24.00</td><td>0.2164</td><td>28.67</td><td>0.8537</td><td>29.25</td><td>0.1933</td></tr><tr><td>DDRM [25]</td><td></td><td>27.90</td><td>0.8317</td><td>12.28</td><td>0.1661</td><td>29.55</td><td>0.8670</td><td>13.10</td><td>0.1426</td></tr><tr><td>DPS [9]</td><td></td><td>20.99</td><td>0.6194</td><td>148.30</td><td>0.5249</td><td>21.44</td><td>0.6598</td><td>148.83</td><td>0.5175</td></tr><tr><td>GDP [19]</td><td></td><td>27.89</td><td>0.8157</td><td>26.56</td><td>0.2724</td><td>28.60</td><td>0.8376</td><td>28.02</td><td>0.2445</td></tr><tr><td>PSLD [39]</td><td></td><td>N/A</td><td>N/A</td><td>N/A</td><td>N/A</td><td>N/A</td><td>N/A</td><td>N/A</td><td>N/A</td></tr><tr><td>DiffIR [52]</td><td></td><td>23.77</td><td>0.6583</td><td>57.23</td><td>0.4687</td><td>23.54</td><td>0.6775</td><td>58.06</td><td>0.4658</td></tr><tr><td>StableSR [47]</td><td></td><td>22.70</td><td>0.6458</td><td>44.87</td><td>0.3039</td><td>23.30</td><td>0.6907</td><td>43.49</td><td>0.2858</td></tr><tr><td>OmniSSR</td><td></td><td>28.57</td><td>0.8540</td><td>13.01</td><td>0.1575</td><td>29.69</td><td>0.8781</td><td>12.99</td><td>0.1459</td></tr><tr><td>Bicubic</td><td></td><td>25.43</td><td>0.7059</td><td>50.84</td><td>0.3755</td><td>25.49</td><td>0.7229</td><td>55.99</td><td>0.3656</td></tr><tr><td>DDRM [25]</td><td></td><td>25.43</td><td>0.7367</td><td>32.69</td><td>0.3206</td><td>25.83</td><td>0.7443</td><td>32.93</td><td>0.3304</td></tr><tr><td>DPS [9]</td><td></td><td>24.75</td><td>0.6594</td><td>120.74</td><td>0.4911</td><td>21.09</td><td>0.6119</td><td>175.2143</td><td>0.5541</td></tr><tr><td>GDP [19]</td><td></td><td>23.16</td><td>0.6692</td><td>77.43</td><td>0.4260</td><td>23.75</td><td>0.6569</td><td>90.23</td><td>0.4240</td></tr><tr><td>PSLD [39]</td><td></td><td>21.72</td><td>0.5498</td><td>107.99</td><td>0.5329</td><td>21.75</td><td>0.5828</td><td>141.49</td><td>0.5461</td></tr><tr><td>DiffIR [52]</td><td></td><td>24.01</td><td>0.6770</td><td>54.14</td><td>0.4367</td><td>23.90</td><td>0.7014</td><td>50.37</td><td>0.4235</td></tr><tr><td>StableSR [47]</td><td></td><td>23.33</td><td>0.6577</td><td>49.95</td><td>0.3135</td><td>23.99</td><td>0.6998</td><td>46.03</td><td>0.3023</td></tr><tr><td>OmniSSR</td><td></td><td>25.77</td><td>0.7279</td><td>30.97</td><td>0.2977</td><td>26.01</td><td>0.7481</td><td>34.58</td><td>0.2963</td></tr></table>
+
+and PSLD [39], which is based on latent diffusion model. We also choose supervised diffusion-based super-resolution approaches including StableSR [47] and DiffIR [52] for comparison. We conduct experiments on  $\times 2$  and  $\times 4$  SR with ERP bicubic downsampling, on ODI-SR test set and SUN test set. We choose WS-PSNR [45], WS-SSIM [67], FID [22], and LPIPS [64] as the main metrics.
+
+Quantitative results are presented in Tab. 1. With proposed OTII and GD correction, OmniSSR out-performs previous methods in terms of both Fidelity (from WS-PSNR and WS-SSIM) and Realness (from FID, LPIPS), which shows superior performance to existing diffusion-based methods for ODISR tasks on different scales.
+
+Qualitative results are shown in Fig. 4 and Fig. 5, which illustrates the visualization of SR results on SUN test set and ODI-SR test set with  $\times 2$  and  $\times 4$  scales, by different methods.
+
+The visual results indicate that our OmniSSR exhibits superior capability for detail recovery compared to other methods, particularly evident in textual elements (e.g., the text "flapping" in upper part of Fig. 4), complex objects (e.g., the black desk with a screen in lower part of Fig. 4, patterns above the white door in lower part of Fig. 5), and small-scale objects (e.g., the person and clock behind the desk in upper part of Fig. 5). OmniSSR demonstrates the ability to recover highly detailed and realistic visual effects from TP images.
+
+# 4.3 Comparison with end-to-end supervised methods
+
+The experiments of comparison in Sec. 4.2 are mainly focused on zero-shot image super-resolution methods, and supervised single image super-resolution methods, where the approaches are not trained or fine-tuned on omnidirectional images. In this part, we will compare OmniSSR to supervised end-to-end methods with end-to-end training on ODI datasets, including SwinIR and OSRT. Besides the
+
+![](images/e4c3ff6c545218f159267cef5882c1603f2c8bf24709a01975de6d37f76fc2e5.jpg)  
+$\therefore m - 1 \neq  0$  ;
+
+![](images/77002c1d00337de418c645adf178eaa35e56c1a3f5ee83e53bfeb849f3eaed87.jpg)  
+HR PSNR/SSIM
+
+![](images/05650ecbcf953f8c27820e324f67b077efd87da717d44393573f8cf043065397.jpg)
+
+![](images/118344129e4a2eb4ef49ca66cfce650818a4e189833fe66125991db2b6b1a1e0.jpg)  
+DPS [9] 24.02dB/0.5849
+
+![](images/d982945947d1b2335bb0a61a5486f3b5c04193c4644c94c0a4f33c6ff494dbe5.jpg)  
+DDRM [25] 30.04dB/0.8855
+
+![](images/82b8f5727c3feb46d1fab616286acba8ecf4e4d130640730b693e03e94627b25.jpg)  
+SUN 360  $(\times 2)$  : 001
+
+![](images/0c3a37a737516d7755f59d9cc28cf2ea82c2e766d12fbad042740b8a943120a8.jpg)  
+GDP [19] 0.07dB/0.8802
+
+![](images/51e080788bc5266f80850c4fd2c7d67f12d19c5ba9a9dd08ba45a05525c3fbc1.jpg)  
+Bicubic 28.67dB/0.8317  
+DiffIR [52] 24.47dB/0.6421
+
+![](images/d7f3e583f56f782601858dfe22fecbd03b12d177240296fb18e580a05c6757d9.jpg)  
+StableSR [47] 23.81dB/0.7384
+
+![](images/026ddc985ebdab03ea056438df9d09337ff31b83aeb64778b6cc93f49748ddad.jpg)  
+OmniSSR(ours) 30.15dB/0.8859
+
+![](images/a22c76f1a9c57cf9ec0cd05b2cf7728d59df730cc8b188461f2ea001da8ec4f1.jpg)  
+SUN 360  $(\times 4)$  : 009  
+GDP [19] 23.02dB/0.7525  
+Fig. 4: Visualized comparison of  $\times 2$  and  $\times 4$  SR results on SUN 360 test set. 001 and 009 are the ID numbers in the test set filenames. We also calculate the PSNR and SSIM to HR ground truth of each SR result and downsampled image.
+
+![](images/ad6731bd33a9a430eda461540342ab99da7f72d821a6de2efa230ce8f2f0bc06.jpg)
+
+![](images/fb91862be9975ce4a1bb80cd177b676301418460af1b0b6581f80b2790e37779.jpg)  
+DiffIR [52] 23.48dB/0.7799
+
+![](images/d8c4ff0b5f831768bbd1ad40dfa23441b9d5b0b105e7250788095ac4b9050705.jpg)  
+StableSR [47] 24.62dB/0.7981
+
+![](images/90267770031892416bc9530e51251ba52189be4ad3bb981dd57eed42669e6495.jpg)  
+OmniSSR(ours) 26.53dB/0.8265
+
+main metrics in Sec. 4.2, we also use NIQE [34] and DISTS [15] to evaluate the visual perception of SR outputs. Results are presented in Tab. 2, which shows that although our OmniSSR exhibits inferior fidelity metrics compared to end-to-end supervised methods trained directly on ODI datasets, it demonstrates notable improvements in the visual quality and authenticity of super-resolved images. Notably, end-to-end methods often produce smoothed reconstructions with distortions, whereas our approach preserves finer details and adheres more closely to the realistic distribution. Considering that our method has never been trained or tuned on ODI datasets, nor having omnidirectional images prior, this result is acceptable.
+
+# 4.4 Ablation Studies
+
+We first sequentially validate the performance improvement of the proposed strategy in OmniSSR including input image type, OTII and GD correction, on the ODI-SR test set with  $\times 2$  SR task, thereby demonstrating the significance of these strategies. The details are demonstrated as follows:
+
+![](images/0bdc65fb2176dac12f5298b3ca6d8fcb1ec664fb7773e237fbc775869930a5f4.jpg)  
+ODI-SR  $(\times 2)$  :067
+
+![](images/0752235e31564fb4f752b63de28f5a0ac4c30935b0e53ef6caf8179ebf4bf697.jpg)  
+HR
+
+![](images/cf16280fa802fd9d07b9ff4c5314099f7089002bddd8c755718783f03a4ed9e0.jpg)  
+Bicubic
+
+![](images/543e56ef5b919493be7a8272c2ca28f09f569e5d38f7712965492320a2909fd0.jpg)  
+DPS [9]
+
+![](images/527ed727580df771e6de1ebb3b5bef03127e6a1ae23209ef51281ef67497fac8.jpg)  
+PSNR/S  
+GDP [19]
+
+![](images/c3b5c7e640014d61d969b992d50e17ef486601a8713b4ea9cf721f5a150659aa.jpg)  
+27.67dB/0.8095  
+DiffIR [52]
+
+![](images/fbb72815f1c1de0e2f751e61bdb32e69dd9832622603ed3647c242b650b3f5c7.jpg)  
+22.93dB/0.5653  
+StableSR [47]
+
+![](images/359bdce6a7e8122ad244e0f897c40642adfd7cd00819502c5a6608ac55d62143.jpg)  
+DDRM [25]
+
+![](images/a39408904009596fae7d49e6a19b743fb7fff6944807c0196b6c4431195a9f4a.jpg)  
+29.91dB/0.8809  
+OmniSSR(ours)
+
+![](images/d9dd5fa49be977880334bf244e60346700d2164088881783a7cfe8c0758cb98c.jpg)  
+ODI-SR  $(\times 4)$  :049  
+Fig. 5: Visualized comparison of  $\times 2$  and  $\times 4$  SR results on ODI-SR test set. 067 and 049 are the ID numbers in test set filenames. We also calculate the PSNR and SSIM between ground truth and each SR result as well as the downsampled image.
+
+![](images/93c9af5a620013e7780e5afa1e28077d1cec55b360e465d761e40a0ee6c62dfb.jpg)  
+HR
+
+![](images/1b960db6d669d53cef5bcf03dc771877d1cca6a1dcbdf7989e6b9516c3c4e692.jpg)  
+22.65dB/0.6248  
+Bicubic
+
+![](images/665b7a80abefad187ae780411eb88428615dd5b47d299a62e7eef6793e7a5976.jpg)  
+21.80dB/0.5892  
+DPS [9]
+
+26.21dB/0.7574
+
+![](images/ceaf816b663668d5f27c284215943761f50a52998a3e3458c9f928edb319c082.jpg)  
+PSNR/SSIM  
+GDP [19] 24.15dB/0.7179
+
+![](images/bfa6cc4eb8ac106c9f8c23112c1587b60c9c8ae66410ef9371f606367ada7017.jpg)  
+25.44dB/0.7536  
+DiffIR [52]  
+.25dB/0.7594
+
+26.21dB/0.7574
+
+26.21dB/0.7574
+
+![](images/2b9a5aceeddd7d72e94d00bc0cc4f0520d244efa9dd886d51156ed52d0128903.jpg)  
+29.99dB/0.8798  
+DDRM [25]
+
+![](images/87f0ff13584b3ff238d34f0281a2ee9ae79b7b540089a9bb21222902908299ec.jpg)  
+27.12dB/0.8129  
+OmniSSR(ours)  
+27.20dB/0.8168
+
+1) we do not use any proposed strategy in the SR task, which is equivalent to the vanilla StableSR baseline;  
+2) we transform the degraded ERP image to TP images and feed them separately into StableSR pipeline, instead of directly inputting ERP images;  
+3) based on 2), we add OTII strategy during the denoising process of SD (Algo. 2 line 7);  
+4) based on 2), we add GD correction at the post-processing stage (Algo. 1 line 12) of the overall pipeline;  
+5) based on 3) and 4), we add GD correction at every step and post-processing stage of sampling, to improve the consistency of the restored result.
+
+Note that the execution of GD correction requires the execution of OTII in the denoising process simultaneously, there is no scenario where only GD correction is executed without the execution of OTII in the denoising process.
+
+Quantitative results of ablation studies are shown in Tab. 3. From the result shown below, we could come to the claim that the OTII helps improve the performance on the domain level, and the transformation between ERP and TP images provides information fusion among adjacent TP images. Our proposal of
+
+Table 2: Comparison on  $\times 4$  SR task with supervised methods trained on ODI-SR dataset, including SwinIR and OSRT. The best results are shown in bold.  
+
+<table><tr><td>Method</td><td>Dataset</td><td>WS-PSNR↑</td><td>WS-SSIM↑</td><td>FID↓</td><td>LPIPS↓</td><td>NIQE↓</td><td>DISTS↓</td></tr><tr><td>SwinIR [29]</td><td></td><td>26.76</td><td>0.7620</td><td>27.94</td><td>0.3321</td><td>5.3961</td><td>0.1710</td></tr><tr><td>OSRT [60]</td><td>ODI-SR</td><td>26.89</td><td>0.7646</td><td>27.39</td><td>0.3258</td><td>5.4364</td><td>0.1695</td></tr><tr><td>OmniSSR</td><td></td><td>25.77</td><td>0.7279</td><td>30.97</td><td>0.2977</td><td>5.2891</td><td>0.1541</td></tr><tr><td>SwinIR [29]</td><td></td><td>26.02</td><td>0.7692</td><td>39.90</td><td>0.3419</td><td>5.2440</td><td>0.1325</td></tr><tr><td>OSRT [60]</td><td>SUN 360</td><td>26.33</td><td>0.7766</td><td>39.22</td><td>0.3364</td><td>5.2984</td><td>0.1312</td></tr><tr><td>OmniSSR</td><td></td><td>26.01</td><td>0.7481</td><td>34.58</td><td>0.2963</td><td>5.1329</td><td>0.1299</td></tr></table>
+
+Table 3: Ablation studies of OmniSSR on input type, OTII, and GD correction, on the test set of the ODI-SR dataset. Best results are shown in bold.  
+
+<table><tr><td>Input type</td><td>OTII</td><td>GD Correction</td><td>WS-PSNR↑</td><td>WS-SSIM↑</td><td>FID↓</td><td>LPIPS↓</td></tr><tr><td>ERP</td><td>×</td><td>×</td><td>22.69</td><td>0.6458</td><td>44.87</td><td>0.3039</td></tr><tr><td>TP</td><td>×</td><td>×</td><td>23.53</td><td>0.6849</td><td>43.91</td><td>0.3113</td></tr><tr><td>TP</td><td>✓</td><td>×</td><td>23.74</td><td>0.6847</td><td>65.35</td><td>0.3748</td></tr><tr><td>TP</td><td>×</td><td>✓ (in post-process only)</td><td>26.77</td><td>0.8192</td><td>15.41</td><td>0.1691</td></tr><tr><td>TP</td><td>✓</td><td>✓</td><td>28.58</td><td>0.8540</td><td>13.01</td><td>0.1575</td></tr></table>
+
+Gradient Decomposition corrects such restoration result, improving fidelity and realness significantly at the same time, and it would be better if it is applied at each step of the overall denoising pipeline. Tab. 4 shows the effect of mitigating information loss via proposed pre-upsampling strategy.
+
+![](images/4da33366ec8ad894a06bf0a9b601184e2fc449d8ab20e8fa199af3a5e6d11cf6.jpg)  
+Fig.6: Ablation of choices on  $\gamma_{p}$ ,  $\gamma_{e}$  and  $\gamma_{l}$ . For better readability, WS-PSNR and LPIPS are chosen as evaluation metrics for fidelity and visual quality, respectively, to demonstrate the performance under different choices of the gamma parameter. We illustrate the results of (a)  $\gamma_{p}$  and  $\gamma_{e}$  fixed, while adjusting  $\gamma_{l}$ ; (b)  $\gamma_{e}$  and  $\gamma_{l}$  fixed, while adjusting  $\gamma_{p}$ ; (c)  $\gamma_{p}$  and  $\gamma_{l}$  fixed, while adjusting  $\gamma_{e}$ . It can be observed that when  $\gamma_{p} = 1$ ,  $\gamma_{e} = 1$ , and  $\gamma_{l} = 0.5$ , OmniSSR achieves the relatively best performance.
+
+![](images/5ad62970d5261ee1e556964b7162f20ac9220f05687d019e45e9cd5af8a3b221.jpg)
+
+![](images/79cf981a76a17afe2ec979ca890f842edaa11cb91a5c60efe71ba9c4ef1a517e.jpg)
+
+For  $\gamma$  in the GD correction technique, we use grid search to obtain better results on ODI-SR dataset and  $\times 4$  SR task. Fig. 6 shows performance on different choices of  $\gamma_{p}$  in Algo. 1 line 12,  $\gamma_{e}$  in Algo. 2 line 8, and  $\gamma_{l}$  in Algo. 2 line 11. The entire ablation of  $\gamma_{p},\gamma_{e}$  and  $\gamma_{l}$ , with WS-PSNR, WS-SSIM, FID and LPIPS score all calculated and compared, will be provided in Supplementary Materials.
+
+To evaluate the generalizability of our proposed modules, including Pre-Upsampling, OTII, and GD correction, we further conducted ablation studies
+
+Table 4: Results of pre-upsampling strategy on different scales, where  $(x,y)$  denotes bicubic-based upsampling at  $x\times$  scale to ERP before  $\mathrm{ERP}\rightarrow \mathrm{TP}$ , and  $y\times$  scale to TP before  $\mathrm{TP}\rightarrow \mathrm{ERP}$  transformation. Best results are shown in bold.  
+
+<table><tr><td>\( \mathrm{{ERP}} \rightarrow  \mathrm{{TP}} \rightarrow  \mathrm{{ERP}} \)</td><td>(1,1)</td><td>(1,4)</td><td>(4,1)</td><td>(4,2)</td><td>(2,4)</td><td>(4,4)</td></tr><tr><td>WS-PSNR↑</td><td>28.98</td><td>38.11</td><td>28.99</td><td>33.91</td><td>38.05</td><td>38.18</td></tr><tr><td>WS-SSIM↑</td><td>0.8859</td><td>0.9838</td><td>0.8862</td><td>0.9626</td><td>0.9837</td><td>0.9841</td></tr></table>
+
+on two super-resolution backbones, StableSR and SwinIR. The results underscore substantial performance enhancements facilitated by our modules across both backbones, which is provided in Supplementary Materials.
+
+# 5 Limitation and Discussion
+
+Although OmniSSR bridges the gap between omnidirectional and planar images, achieving competitive performance and better visual results in ODISR, it still exhibits the following limitations: (1) The inference of the diffusion model requires a considerable amount of time, approximately 14 minutes per ERP-formatted omnidirectional image to be super-resolved into size  $1024 \times 2048$ , making real-time super-resolution challenging; (2) Multiple conversions between ERP and TP are required in the pipeline, leading to improved performance but consuming additional inference time; (3) Further exploration of the convex optimization properties of GD correction is warranted, such as designing gradient term coefficients adaptive to reconstruction results and degradation types.
+
+This study explores the application of image generation models to ODISR tasks. In future work, the framework behind OmniSSR can be extended beyond the confines of image super-resolution in a single scenario and venture into more complex ODI-based real-world scenarios. These include ODI editing, ODI inpainting, enhancing the quality of 3D Gaussian Splatting scenes [26,41] obtained after super-resolving ERP images, as well as enhancing the quality of omnidirectional videos [48], and other possible diffusion-based applications [57,68].
+
+# 6 Conclusion
+
+This paper leverages the image prior of Stable Diffusion (SD) and employs the Octadecaplex Tangent Information Interaction (OTII) to achieve zero-shot omnidirectional image super-resolution. Additionally, we propose the Gradient Decomposition (GD) correction based on convex optimization algorithms to refine the initial super-resolution results, enhancing the fidelity and realness of the restored images. The superior performance of our proposed method, OmniSSR, is demonstrated on benchmark datasets. By bridging the gap between omnidirectional and planar images, we establish a training-free approach, mitigating the data demand and over-fitting associated with end-to-end training. The application scope of our method can be further extended to various applications, presenting potential value across multiple visual tasks.
+
+# References
+
+1. An, H., Zhang, X.: Perception-oriented omnidirectional image super-resolution based on transformer network. In: Proceedings of the IEEE International Conference on Image Processing (ICIP) (2023)  
+2. Arican, Z., Frossard, P.: Joint registration and super-resolution with omnidirectional images. IEEE Transactions on Image Processing (TIP) (2011)  
+3. Chan, K.C., Xu, X., Wang, X., Gu, J., Loy, C.C.: Glean: Generative latent bank for image super-resolution and beyond. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) (2022)  
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