Update

PAL_intrinsic_calib_results.txt

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+#polynomial coefficients for the DIRECT mapping function (ocam_model.ss in MATLAB). These are used by cam2world
+
+5 -5.182068e+02 0.000000e+00 4.106521e-04 4.964961e-07 -1.263977e-10 
+
+#polynomial coefficients for the inverse mapping function (ocam_model.invpol in MATLAB). These are used by world2cam
+
+14 837.987416 583.296200 139.362253 153.600190 88.238626 58.649061 51.895338 27.443561 5.105123 5.512953 22.238375 25.200650 11.715910 1.986661 
+
+#center: "row" and "column", starting from 0 (C convention)
+
+966.412941 1302.979238
+
+#affine parameters "c", "d", "e"
+
+1.000051 -0.000095 0.000031
+
+#image size: "height" and "width"
+
+1944 2592
+

README.md

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+# QuadOcc
+
+QuadOcc is a real-world dataset for 3D semantic occupancy prediction on legged robots, captured using a quadruped equipped with a Panoramic Annular Lens (PAL) camera and LiDAR. This document describes the images, point clouds, voxel labels, and supporting files in the public data package.
+
+## 1. Dataset Overview
+
+| Property | Description |
+| --- | --- |
+| Scale | 10 scenes and approximately 24,000 frames |
+| Lighting | Day, dusk, and night |
+| Image representations | Raw annular images and unfolded equirectangular panoramas |
+| Voxel grid | `64 × 64 × 8` |
+| Voxel size | `0.4 m × 0.4 m × 0.4 m` |
+| Grid axes | X forward, Y left, Z up |
+| Default spatial bounds | X and Y: `[-12.8, 12.8] m`; Z: `[-2.0, 1.2] m` |
+| Six semantic categories | Vehicle, pedestrian, road, building, vegetation, terrain |
+
+The spatial bounds above follow the defaults in [Appendix Section 9.7](https://arxiv.org/html/2511.03571v2#S9.SS7). Check the accompanying data configuration when loading a specific release.
+
+## 2. Directory Structure
+
+```text
+QuadOcc/
+├── sequences/
+│   ├── 2024-06-20-20-46-59/
+│   │   ├── images_raw/
+│   │   ├── images_unfold/
+│   │   ├── lidar/
+│   │   ├── lidar_input/
+│   │   ├── unlabel/
+│   │   ├── voxel_final/
+│   │   ├── PAL_intrinsic_calib_results.txt
+│   │   ├── pcd_position.txt
+│   │   └── timestamp_relative.txt
+│   └── .../
+├── maps_370.npz
+├── PAL_intrinsic_calib_results.txt
+└── README.md
+```
+
+
+## 3. Root-Level Files
+
+| Name | Description |
+| --- | --- |
+| `sequences/` | Data directories for individual capture sequences. |
+| `maps_370.npz` | Supporting data in NPZ format. Array keys, shapes, and purpose must be determined from the file contents and accompanying loader. |
+| `PAL_intrinsic_calib_results.txt` | PAL camera intrinsics stored at the package root. |
+| `README.md` | Dataset directory and file documentation. |
+
+## 4. Per-Sequence Files
+
+All paths below are relative to `sequences/<sequence_id>/`.
+
+### 4.1 Images
+
+| Directory | Contents |
+| --- | --- |
+| `images_raw/` | Original annular images captured by the PAL camera. |
+| `images_unfold/` | Panoramic images unfolded using the PAL calibration parameters. |
+
+The paper uses the Taylor/OCam camera model to unfold annular images into equirectangular panoramas. See [Appendix Section 9.1](https://arxiv.org/html/2511.03571v2#S9.SS1).
+
+### 4.2 Point Clouds and Voxels
+
+| Directory | Contents |
+| --- | --- |
+| `lidar/` | LiDAR scan point clouds. |
+| `lidar_input/` | Binary voxel inputs constructed from LiDAR points inside the bounding volume. Stored as `.npy` arrays with shape `(64, 64, 8)`: `1` indicates that a voxel contains points, and `0` indicates that it contains no points. |
+| `unlabel/` | Locations occupied in the LiDAR scan representation that have no annotation in the final semantic labels. |
+| `voxel_final/` | Final semantic voxel labels with grid shape `(64, 64, 8)`. |
+
+`lidar_input/` represents scan-point occupancy, `voxel_final/` provides semantic supervision, and `unlabel/` identifies unannotated locations. A binary input value of `0` only means that no scan point falls inside the voxel; it does not establish free space. Unannotated locations must not automatically be treated as free space either.
+
+The six semantic category names are `vehicle`, `pedestrian`, `road`, `building`, `vegetation`, and `terrain`. Their listing order does not define numeric label IDs. Use the accompanying label mapping and loading configuration for class IDs, empty-voxel encoding, and ignore values. An example configuration is provided as `quadocc.yaml` in the release.
+
+### 4.3 Timestamps, Poses, and Camera Intrinsics
+
+| File | Contents |
+| --- | --- |
+| `timestamp_relative.txt` | Relative timestamps for the sequence. |
+| `pcd_position.txt` | Point-cloud poses synchronized with the sequence data. |
+| `PAL_intrinsic_calib_results.txt` | PAL camera intrinsics stored within the sequence directory. |
+
+Check image, point-cloud, and label correspondence using timestamps and the loading configuration. Pose column order, rotation representation, transform direction, and selection between root-level and sequence-level calibration files depend on the actual files and accompanying code.
+
+## 5. Semantic Label Construction
+
+The labels are constructed using panoramic semantic segmentation, image-to-point semantic transfer, multi-frame point-cloud aggregation, and manual corrections.
+
+## 6. References
+
+- [OneOcc: Semantic Occupancy Prediction for Legged Robots with a Single Panoramic Camera](https://arxiv.org/abs/2511.03571)
+- [Appendix: QuadOcc Dataset Construction](https://arxiv.org/html/2511.03571v2#S9)
+- [Official OneOcc Repository](https://github.com/MasterHow/OneOcc)
+
+## 7. Citation
+
+If you use the QuadOcc dataset in your research, please cite OneOcc. Thank you!
+
+```bibtex
+@InProceedings{Shi_2026_CVPR,
+  author    = {Shi, Hao and Wang, Ze and Guo, Shangwei and Duan, Mengfei and Wang, Song and Chen, Teng and Yang, Kailun and Wang, Lin and Wang, Kaiwei},
+  title     = {{OneOcc}: Semantic Occupancy Prediction for Legged Robots with a Single Panoramic Camera},
+  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+  month     = {June},
+  year      = {2026},
+  pages     = {14229--14240}
+}
+```

maps_370.npz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d36cc91b0c92ab5e815967350e72d1d605e14be300f88f97a107e35b8c835059
+size 43922982

quadocc.yaml

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+# This file is covered by the LICENSE file in the root of this project.
+nbr_classes: 7
+grid_dims: [64, 8, 64]  # (W, H, D)
+labels:
+  0 : "unlabeled"
+  1 : "vehicle"
+  2 : "bus"
+  3 : "bicycle"
+  4 : "person"
+  5 : "road"
+  6 : "parking"
+  7 : "sidewalk"
+  8 : "building"
+  9 : "fence"
+  10: "vegetation"
+  11: "trunk"
+  12: "terrain"
+  13: "pole"
+  14: "traffic sign"
+color_map: # bgr
+  0 : [0, 0, 0]
+  1 : [245, 150, 100]
+  2 : [250, 80, 100]
+  3 : [245, 230, 100]
+  4 : [30, 30, 255]
+  5 : [255, 0, 255]
+  6 : [255, 150, 255]
+  7 : [75, 0, 75]
+  8 : [0, 200, 255]
+  9 : [50, 120, 255]
+  10: [0, 175, 0]
+  11: [0, 60, 135]
+  12: [80, 240, 150]
+  13: [150, 240, 255]
+  14: [0, 0, 255]
+content:
+  0: 0.2345460696
+  1: 0.0004825479
+  2: 0.0097090973
+  3: 0.4448292373
+  4: 0.0619459724
+  5: 0.1258040592
+  6: 0.0684332385
+learning_map: # consider bus map to car
+  0 : 255     # unlabeled, ignored
+  1 : 1       # car/vehicle
+  2 : 255     # bus, ignored
+  3 : 255     # bicycle, ignored
+  4 : 2       # person
+  5 : 3       # road
+  6 : 255     # parking, ignored
+  7 : 255     # sidewalk, ignored
+  8 : 4       # building
+  9 : 255     # fence, ignored
+  10: 5       # vegetation
+  11: 255     # trunk, ignored
+  12: 6       # terrain
+  13: 255     # pole, ignored
+  14: 255     # traffic sign, ignored
+  255: 0
+learning_map_inv: # inverse of previous map
+  0 : 0
+  1 : 1
+  2 : 4
+  3 : 5
+  4 : 8
+  5 : 10
+  6 : 12
+learning_ignore: # ignore classes
+  0 : True
+  1 : False
+  2 : False
+  3 : False
+  4 : False
+  5 : False
+  6 : False
+  7 : False
+  8 : False
+  9 : False
+  10: False
+  11: False
+  12: False
+  13: False
+  14: False
+split: # sequence numbers
+  train:
+    - 2024-06-20-20-49-54
+    - 2024-06-20-20-52-21
+    - 2024-06-20-21-05-40
+    - 2024-06-20-21-19-23
+    - 2024-06-20-21-15-21
+    - 2024-06-20-21-05-05
+    - 2024-06-20-21-11-28
+  valid:
+    - 2024-06-20-21-16-11
+    - 2024-06-20-21-08-40
+    - 2024-06-20-20-46-59
+  test:
+    - NA