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

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gtdong-ustc/LiDARSceneFlow

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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}
}

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

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