Awesome
<div align="center"> <!-- omit in toc -->Scene as Occupancy
</div>We believe Occupancy serves as a general representation of the scene and could facilitate perception and planning in the full-stack of autonomous driving.
https://github.com/OpenDriveLab/OccNet/assets/54334254/92fb43a0-0ee8-4eab-aa53-0984506f0ec3
<!-- omit in toc -->Scene as Occupancy
- Paper in arXiv | CVPR 2023 AD Challenge Occupancy Track
- Point of contact: simachonghao@pjlab.org.cn
3D Occupancy Prediction Leaderboard
We provide a full-scale 3D occupancy leaderboard based on the CVPR 2023 Autonomous Driving challenge. Top entries (by 06-09-2023) are provided below. Check the website out!
Table of Contents
Highlights
- :oncoming_automobile: General Representation in Perception: 3D Occupancy is a geometry-aware representation of the scene. Compared to the form of 3D bounding box & BEV segmentation, 3D occupancy could capture the fine-grained details of critical obstacles in the scene.
- :trophy: Exploration in full-stack Autonomous Driving: OccNet, as a strong descriptor of the scene, could facilitate subsequent tasks such as perception and planning, achieving results on par with LiDAR-based methods (41.08 on mIOU in 3D occupancy, 60.46 on mIOU in LiDAR segmentation, 0.703 avg.Col in motion planning).
News
- [2023/06/06] Paper released on arxiv
- [2023/06/04] Code & model initial release
v1.0 - [2023/06/04] 3D Occupancy and flow dataset release
v1.0 - [2023/06/01] CVPR AD Challenge 3D Occupancy Track close
- [2023/03/01] CVPR AD Challenge 3D Occupancy Track launch
Getting Started
Results and Pre-trained Models
We will release pre-trained weight soon.
TODO List
- 3D Occupancy and flow dataset
v1.0 - 3D Occupancy Prediction code
v1.0 - Pre-trained Models
- Occupancy label generation code
- GT label with more voxel size
- Compatibility with other BEV encoders
License & Citation
All assets (including figures) and code are under the Apache 2.0 license unless specified otherwise. The data license inherits the license used in nuScenes dataset.
Please consider citing our paper if the project helps your research with the following BibTex:
@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}
}
Challenge
We host the first 3D occupancy prediciton challenge on CVPR 2023 End-to-end Autonomous Driving Workshop. For more information about the challenge, please refer to here.
Related resources
