Awesome

<h1> Deep Shells: Unsupervised Shape Correspondence with Optimal Transport </h1>

Implementation of the NeurIPS 2020 paper (arXiv). For a given pair of deformable 3D shapes, our algorithm produces high-quality correspondences.

Usage

Preprocessing

To preprocess the raw datasets, run the matlab file:

preprocess_data/preprocess_dataset.m

Datasets

In our experiments, we use the FAUST remeshed and SCAPE remeshed benchmarks. Both datasets can be downloaded from here.
Change dataset paths under the get_faustremeshed_file() and get_scaperemeshed_file() functions in data.py.
To jointly train multiple datasets (including inter-dataset pairs), create a hybrid dataset, see e.g. FaustScapeRemeshedTrain in data.py. In the paper, we show an experiment where we jointly train on FAUST remeshed and SCAPE remeshed (see Table 1).

Models

Checkpoints are saved under:

./models/

The checkpoint in ./models/Faust_Scape/ corresponds to our results in the 5th and 6th column of Table 1.

Train

For FAUST remeshed, run train_faustremeshed_train() from main.py.
For SCAPE remeshed, run train_scaperemeshed_train() from main.py.

Test

To output test correspondences for one sample pair, see demo_faust_scape() in main.py.
Run your own examples analogously.

Citation

If you use our implementation, please cite:

@article{eisenberger2020deep,
  title={Deep Shells: Unsupervised Shape Correspondence with Optimal Transport},
  author={Eisenberger, Marvin and Toker, Aysim and Leal-Taix{\'e}, Laura and Cremers, Daniel},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  year={2020}
}

Axiomatic matching (smooth shells)

The repo also contains a vanilla implementation of the CVPR 2020 paper smooth shells (the original implementation is in matlab).
If you use this part of our implementation, cite the smooth shells paper:

@inproceedings{eisenberger2020smooth,
  title={Smooth Shells: Multi-Scale Shape Registration with Functional Maps},
  author={Eisenberger, Marvin and Lahner, Zorah and Cremers, Daniel},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={12265--12274},
  year={2020}
}