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Unsupervised Deep Multi-Shape Matching (ECCV 2022)

First method for unsupervised multi-shape matching with theoretical cycle-consistency guarantee.

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Installation

conda create -n fmnet python=3.8 # create new viertual environment
conda activate fmnet
conda install pytorch cudatoolkit=11.3 -c pytorch # install pytorch
conda install pyg -c pyg -c conda-forge # install torch_geometric
pip install -r requirements.txt # install other necessary libraries via pip

Data preparation

Currently, the repository supports training and testing on FAUST remesh and SCAPE remesh dataset. The triangle mesh data and the ground truth correspondence can be downloaded through this link. To compute SHOT descriptor as network input, we use pyshot. For data preprocessing, we provide preprocess.py to compute all things we need (LB eigenfunctions, shot descriptor, geodesic matrix). Here is an example for FAUST remesh data. The provided data should have a subfolder off containing all triangle mesh files.

python preprocess.py --data_root ../data/FAUST_r/

The data preprocessing to train deep shell, please refer to the original implementation in here.

After preprocessing, the data looks like this.

├── data
    ├── FAUST_r
        ├── off # .off contains triangle mesh files
        ├── spectral # .mat contains Laplace-Beltrami eigenfunctions
        ├── shot # .mat contains SHOT feature descriptor
        ├── corrs # .vts contains ground truth point to point correspondences
        ├── diffusion # .mat contains spectral operators needed for DiffusionNet
        ├── dist # .mat contains geodesic distance matrices for evaluation

Because the SHOT descriptor computation depends on pyshot, please refer pyshot repository to install it.

The pre-computed SHOT descriptors for FAUST and SCAPE datasets in this paper can be found in the following links:

FAUST_r

SCAPE_r

Train

To train a model for 3D shape matching. You only need to write or use a YAML config file. In the YAML config file, you can specify everything around training. Here is an example to train.

python train.py --opt options/train.yaml

You can visualize the training process via TensorBoard.

tensorboard --logdir experiments/

Test

After finishing training. You can evaluate the model performance using a YAML config file similar to training. Here is an example to evaluate.

python test.py --opt options/test.yaml

Pretrained models

You can find the pre-trained models on FAUST and SCAPE datasets in checkpoints for reproducibility.

Acknowledgement

The implementation of DiffusionNet is modified from the official implementation, the computation of the FM unsupervised loss and FM solvers are modified from SURFMNet-pytorch and DPFM, the Sinkhorn normalization is adapted from tensorflow implementation.
We thank the authors for making their codes publicly available.

Citation

If you find the code is useful, please cite the following paper

@inproceedings{cao2022,
title = {Unsupervised Deep Multi-Shape Matching},
author = {D. Cao and F. Bernard},
year  = {2022},
booktitle = {European Conference on Computer Vision (ECCV)}
}