| --- |
| license: mit |
| task_categories: |
| - other |
| tags: |
| - autonomous-driving |
| - collaborative-perception |
| - 3d-semantic-occupancy |
| - carla |
| --- |
| |
| # Co3SOP: A Synthetic Benchmark for Collaborative 3D Semantic Occupancy Prediction in V2X Autonomous Driving |
|
|
| [**Paper**](https://huggingface.co/papers/2506.17004) | [**GitHub**](https://github.com/tlab-wide/Co3SOP) |
|
|
| Co3SOP is a high-resolution synthetic benchmark designed for **Collaborative 3D Semantic Occupancy Prediction** in V2X-enabled autonomous driving. |
|
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| While single-vehicle perception is often limited by occlusions, restricted sensor range, and narrow viewpoints, Co3SOP facilitates research into collaborative perception. The dataset provides dense and comprehensive occupancy annotations generated using a high-resolution semantic voxel sensor in the CARLA simulator, replaying existing collaborative perception scenarios. |
|
|
| ## Dataset Features |
| - **High-Resolution Annotations:** Provides a voxel-level representation of both geometric details and semantic categories. |
| - **V2X Scenarios:** Enables the exchange of information between multiple agents to enhance perception accuracy. |
| - **Diverse Prediction Ranges:** Establishes benchmarks with varying spatial extents (25.6m, 51.2m, and 76.8m) to assess the impact of range on collaborative prediction. |
|
|
| ## Citation |
| If you find this dataset or research useful, please consider citing: |
|
|
| ```bibtex |
| @article{wu2025synthetic, |
| title={A Synthetic Benchmark for Collaborative 3D Semantic Occupancy Prediction in V2X Autonomous Driving}, |
| author={Wu, Hanlin and Lin, Pengfei and Javanmardi, Ehsan and Bao, Naren and Qian, Bo and Si, Hao and Tsukada, Manabu}, |
| journal={arXiv preprint arXiv:2506.17004}, |
| year={2025} |
| } |
| ``` |
|
|
| ## Acknowledgements |
| This work builds upon several excellent open-source projects, including [OpenCOOD](https://github.com/DerrickXuNu/OpenCOOD), [SurroundOcc](https://github.com/weiyithu/SurroundOcc), and [LMSCNet](https://github.com/astra-vision/LMSCNet). |
