/LiDARSceneFlow

[CVPR 2022] "Exploiting Rigidity Constraints for LiDAR Scene Flow Estimation"

Primary LanguagePythonMIT LicenseMIT

This is the code for "Exploiting Rigidity Constraints for LiDAR Scene Flow Estimation".

Prerequisities

Our model is trained and tested under:

  • Python 3.6.9
  • NVIDIA GPU + CUDA CuDNN
  • PyTorch (torch == 1.5)
  • scipy
  • tqdm
  • sklearn
  • numba
  • cffi
  • pypng
  • pptk

Compile the furthest point sampling, grouping and gathering operation for PyTorch. We use the operation from this repo.

cd pointnet2
python setup.py install
cd ../

Train

Set data_root in the configuration file to SAVE_PATH in the data preprocess section. Then run

python train.py config_train.yaml

Citation

If you use this code for your research, please cite our paper.

@inproceedings{dong2022exploiting,
  title={Exploiting rigidity constraints for lidar scene flow estimation},
  author={Dong, Guanting and Zhang, Yueyi and Li, Hanlin and Sun, Xiaoyan and Xiong, Zhiwei},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={12776--12785},
  year={2022}
}