Add paper link, GitHub link and metadata (#1)

- Add paper link, GitHub link and metadata (cc16d9ba1a60c577a585b2529f3544d678aeae09)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,3 +1,38 @@
----
-license: mit
----
+---
+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).