Awesome
RenderOcc
Paper | Video | Technical Report (UniOcc)
(Visualization of RenderOcc's prediction, which is supervised only with 2D labels.)
INTRODUCTION
RenderOcc is a novel paradigm for training vision-centric 3D occupancy models only with 2D labels. Specifically, we extract a NeRF-style 3D volume representation from multi-view images, and employ volume rendering techniques to establish 2D renderings, thus enabling direct 3D supervision from 2D semantics and depth labels.
Getting Started
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Train
# Train RenderOcc with 8 GPUs ./tools/dist_train.sh ./configs/renderocc/renderocc-7frame.py 8
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Evaluation
# Eval RenderOcc with 8 GPUs ./tools/dist_test.sh ./configs/renderocc/renderocc-7frame.py ./path/to/ckpts.pth 8
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Visualization
# Dump predictions bash tools/dist_test.sh configs/renderocc/renderocc-7frame.py renderocc-7frame-12e.pth 1 --dump_dir=work_dirs/output # Visualization (select scene-id) python tools/visualization/visual.py work_dirs/output/scene-xxxx
(The pkl file needs to be regenerated for visualization.)
Model Zoo
Method | Backbone | 2D-to-3D | Lr Schd | GT | mIoU | Config | Log | Download |
---|---|---|---|---|---|---|---|---|
RenderOcc | Swin-Base | BEVStereo | 12ep | 2D | 24.46 | config | log | model |
- More model weights will be released later.
Acknowledgement
Many thanks to these excellent open source projects:
Related Projects:
BibTeX
If this work is helpful for your research, please consider citing:
@article{pan2023renderocc,
title={RenderOcc: Vision-Centric 3D Occupancy Prediction with 2D Rendering Supervision},
author={Pan, Mingjie and Liu, Jiaming and Zhang, Renrui and Huang, Peixiang and Li, Xiaoqi and Liu, Li and Zhang, Shanghang},
journal={arXiv preprint arXiv:2309.09502},
year={2023}
}