| # QuadOcc |
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| 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. |
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| ## 1. Dataset Overview |
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| | 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 | |
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| 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. |
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| ## 2. Directory Structure |
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| ```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 |
| ``` |
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| ## 3. Root-Level Files |
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| | 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. | |
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| ## 4. Per-Sequence Files |
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| All paths below are relative to `sequences/<sequence_id>/`. |
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| ### 4.1 Images |
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| | Directory | Contents | |
| | --- | --- | |
| | `images_raw/` | Original annular images captured by the PAL camera. | |
| | `images_unfold/` | Panoramic images unfolded using the PAL calibration parameters. | |
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| 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). |
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| ### 4.2 Point Clouds and Voxels |
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| | 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)`. | |
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| `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. |
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| 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. |
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| ### 4.3 Timestamps, Poses, and Camera Intrinsics |
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| | 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. | |
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| 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. |
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| ## 5. Semantic Label Construction |
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| The labels are constructed using panoramic semantic segmentation, image-to-point semantic transfer, multi-frame point-cloud aggregation, and manual corrections. |
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| ## 6. References |
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| - [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) |
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| ## 7. Citation |
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| If you use the QuadOcc dataset in your research, please cite OneOcc. Thank you! |
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| ```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} |
| } |
| ``` |
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