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. Check the accompanying data configuration when loading a specific release.

2. Directory Structure

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.

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
• Appendix: QuadOcc Dataset Construction
• Official OneOcc Repository

7. Citation

If you use the QuadOcc dataset in your research, please cite OneOcc. Thank you!

@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}
}