/DeformationPyramid

[NeurIPS 2022] Non-rigid Point Cloud Registration with Neural Deformation Pyramid

Primary LanguagePython

Non-rigid Point Cloud Registration with Neural Deformation Pyramid [Paper]

Hierarchical non-rigid registration of multiple scans

drawing

Scale variant non-rigid registration with Sim(3) warp field

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Requirements

The code tested on python=3.8.10, pytorch=1.9.0 with the following packages:

  • pytorch3d, open3d, opencv-python, tqdm, mayavi, easydict

Obtain the 4DMatch benchmark

  • Download the train/val/4DMatch-F/4DLoMatch-F split, (google drive, 14G). We filter point cloud pairs with near-rigid motions from the original 4DMatch benchmark. 4DMatch-F & 4DLoMatch-F denote the filtered benchmark.
  • Extract it and create a soft link under this repository.
ln -s /path/to/4Dmatch  ./data

Reproduce the result of NDP (no-learned)

  • Run
python eval_nolearned.py --config config/NDP.yaml

To visualize the registration result, add --visualize.

Reproduce the result of LNDP (supervised)

  • First download pre-trained point cloud matching and outlier rejection models (google drive, 271M). Move the models to correspondence/pretrained
  • Install KPConv
cd correspondence/cpp_wrappers; sh compile_wrappers.sh; cd ../..
  • Finally run
python eval_supervised.py --config config/LNDP.yaml

To visualize the registration result, add --visualize.

Run shape transfer example

python shape_transfer.py -s sim3_demo/AlienSoldier.ply -t sim3_demo/Ortiz.ply

Our related projects

Lepard: rabbityl/lepard
DeformingThings4D: rabbityl/DeformingThings4D

Citation

If you use our code please cite:

@article{li2022DeformationPyramid, 
    title={Non-rigid Point Cloud Registration with Neural Deformation Pyramid.}, 
    author={Yang Li and Tatsuya Harada},
    journal={arXiv preprint arXiv:2205.12796},
    year={2022}
}