GitHub - OpenDriveLab/OpenScene: 3D Occupancy Prediction Benchmark in Autonomous Driving
OpenScene is a compact redistribution of the large-scale nuPlan dataset, retaining only relevant annotations and sensor data at 2Hz. This reduces the dataset size by a factor of >10. We cover a wide span of over 120 hours, and provide additional occupancy labels collected in various cities, from Boston, Pittsburgh, Las Vegas to Singapore.
Please consider citing our paper if the project helps your research with the following BibTex:
@inproceedings{yang2024vidar, title={Visual Point Cloud Forecasting enables Scalable Autonomous Driving}, author={Yang, Zetong and Chen, Li and Sun, Yanan and Li, Hongyang}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, year={2024} } @misc{openscene2023, title={OpenScene: The Largest Up-to-Date 3D Occupancy Prediction Benchmark in Autonomous Driving}, author={OpenScene Contributors}, howpublished={\url{https://github.com/OpenDriveLab/OpenScene}}, year={2023} } @article{sima2023_occnet, title={Scene as Occupancy}, author={Chonghao Sima and Wenwen Tong and Tai Wang and Li Chen and Silei Wu and Hanming Deng and Yi Gu and Lewei Lu and Ping Luo and Dahua Lin and Hongyang Li}, year={2023}, eprint={2306.02851}, archivePrefix={arXiv}, primaryClass={cs.CV} }