README.md

---
language:
- en
license: apache-2.0
size_categories:
- 10K<n<100K
task_categories:
- image-text-to-text
pretty_name: Spatial457
tags:
- spatial-reasoning
- multimodal
---

<div align="center">
  <img src="https://xingruiwang.github.io/projects/Spatial457/static/images/icon_name.png" alt="Spatial457 Logo" width="240"/>
</div>

<h1 align="center">
  <a href="https://arxiv.org/abs/2502.08636">
    Spatial457: A Diagnostic Benchmark for 6D Spatial Reasoning of Large Multimodal Models
  </a>
</h1>

<p align="center">
  <a href="https://xingruiwang.github.io/">Xingrui Wang</a><sup>1</sup>,
  <a href="#">Wufei Ma</a><sup>1</sup>,
  <a href="#">Tiezheng Zhang</a><sup>1</sup>,
  <a href="#">Celso M. de Melo</a><sup>2</sup>,
  <a href="#">Jieneng Chen</a><sup>1</sup>,
  <a href="#">Alan Yuille</a><sup>1</sup>
</p>

<p align="center">
  <sup>1</sup> Johns Hopkins University &nbsp;&nbsp;&nbsp;&nbsp;
  <sup>2</sup> DEVCOM Army Research Laboratory
</p>

<p align="center">
  <a href="https://xingruiwang.github.io/projects/Spatial457/">🌐 Project Page</a> •
  <a href="https://arxiv.org/abs/2502.08636">📄 Paper</a> •
  <a href="https://huggingface.co/datasets/RyanWW/Spatial457">🤗 Dataset</a> •
  <a href="https://github.com/XingruiWang/Spatial457">💻 Code</a>
</p>

<p align="center">
  <img src="https://xingruiwang.github.io/projects/Spatial457/static/images/teaser.png" alt="Spatial457 Teaser" width="80%"/>
</p>

---

## 🧠 Introduction

**Spatial457** is a diagnostic benchmark designed to evaluate **6D spatial reasoning** in large multimodal models (LMMs). It systematically introduces four core spatial capabilities:

- 🧱 Multi-object understanding  
- 🧭 2D spatial localization  
- 📦 3D spatial localization  
- 🔄 3D orientation estimation  

These are assessed across **five difficulty levels** and **seven diverse question types**, ranging from simple object queries to complex reasoning about physical interactions.

---

## 📂 Dataset Structure

The dataset is organized as follows:

```
Spatial457/
├── images/                     # RGB images used in VQA tasks
├── questions/                  # JSONs for each subtask
│   ├── L1_single.json
│   ├── L2_objects.json
│   ├── L3_2d_spatial.json
│   ├── L4_occ.json
│   └── ...
├── Spatial457.py               # Hugging Face dataset loader script
├── README.md                   # Documentation
```

Each JSON file contains a list of VQA examples, where each item includes:

- "image_filename": image file name used in the question  
- "question": natural language question  
- "answer": boolean, string, or number  
- "program": symbolic program (optional)  
- "question_index": unique identifier  

This modular structure supports scalable multi-task evaluation across levels and reasoning types.

---

## 🛠️ Dataset Usage

You can load the dataset directly using the Hugging Face 🤗 `datasets` library:

### 🔹 Load a specific subtask (e.g., L5_6d_spatial)

```python
from datasets import load_dataset

# Load dataset from Hugging Face Hub
dataset = load_dataset(
    "RyanWW/Spatial457", 
    name="L5_6d_spatial", 
    split="validation",
    trust_remote_code=True  # Required for custom loading script
)
```

**Important Notes:**
- ✅ Use `trust_remote_code=True` to enable the custom dataset loading script
- ❌ Do NOT use `data_dir` parameter when loading from Hugging Face Hub
- 📦 Images are automatically downloaded and cached

Each example is a dictionary like:

```python
{
  'image': <PIL.Image.Image>,
  'image_filename': 'superCLEVR_new_000001.png',
  'question': 'Is the large red object in front of the yellow car?',
  'answer': 'True',
  'program': [...],
  'question_index': 100001
}
```

### 🔹 Available Configurations

```python
configs = [
  "L1_single",      # Single object identification
  "L2_objects",     # Multi-object understanding
  "L3_2d_spatial",  # 2D spatial reasoning
  "L4_occ",         # Object occlusion
  "L4_pose",        # 3D pose estimation
  "L5_6d_spatial",  # 6D spatial reasoning
  "L5_collision"    # Collision detection
]
```

You can swap `name="..."` in `load_dataset(...)` to evaluate different spatial reasoning capabilities.

### 🔹 Example: Load and Use

```python
from datasets import load_dataset

# Load dataset
dataset = load_dataset(
    "RyanWW/Spatial457",
    name="L5_6d_spatial",
    split="validation",
    trust_remote_code=True
)

print(f"Number of examples: {len(dataset)}")

# Access first example
example = dataset[0]
print(f"Question: {example['question']}")
print(f"Answer: {example['answer']}")
print(f"Image size: {example['image'].size}")
```

## 📊 Benchmark

We benchmarked a wide range of state-of-the-art models—including GPT-4o, Gemini, Claude, and several open-source LMMs—across all subsets. The results below have been updated after rerunning the evaluation. While they show minor variance compared to the results in the published paper, the conclusions remain unchanged.

The inference script supports [VLMEvalKit](https://github.com/open-compass/VLMEvalKit) and is run by setting the dataset to `Spatial457`. You can find the detailed inference scripts [here](https://github.com/XingruiWang/VLMEvalKit).


### Spatial457 Evaluation Results 

| Model                  | L1_single | L2_objects | L3_2d_spatial | L4_occ | L4_pose | L5_6d_spatial | L5_collision |
|------------------------|-----------|------------|---------------|--------|---------|----------------|---------------|
| **GPT-4o**             | 72.39     | 64.54      | 58.04         | 48.87  | 43.62   | 43.06          | 44.54         |
| **GeminiPro-1.5**      | 69.40     | 66.73      | 55.12         | 51.41  | 44.50   | 43.11          | 44.73         |
| **Claude 3.5 Sonnet**  | 61.04     | 59.20      | 55.20         | 40.49  | 41.38   | 38.81          | 46.27         |
| **Qwen2-VL-7B-Instruct** | 62.84   | 58.90      | 53.73         | 26.85  | 26.83   | 36.20          | 34.84         |

---

## 📚 Citation

```bibtex
@inproceedings{wang2025spatial457,
  title     = {Spatial457: A Diagnostic Benchmark for 6D Spatial Reasoning of Large Multimodal Models},
  author    = {Wang, Xingrui and Ma, Wufei and Zhang, Tiezheng and de Melo, Celso M and Chen, Jieneng and Yuille, Alan},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year      = {2025},
  url       = {https://arxiv.org/abs/2502.08636}
}
```