---
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. 

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).