Introduction

This repository contains the code for the paper Non-Rigid Shape Registration via Deep Functional Maps Prior

This code is under construction. The final version of code will be released soon.

Installation

To install requirements:

pip install -r requirements.txt

Installing PyTorch may require an ad hoc procedure, depending on your computer settings.

Dataset

We have uploaded the SCAPE_r dataset. And you can make your own dataset like:

SCAPE_r
--shapes_train
--shapes_test
--corres(vts files, if not, you should delete the vts_list in dataset.py)

Training

In the DFM folder, run the following command to train our modified DGCNN model on the train set:

python train.py

Evaluation

In the registration folder, run the following command to evaluate the trained model on the test set:

python test.py

the results will be saved in the results folder.

License

If you use this code, please cite our paper.

@inproceedings{NEURIPS2023_b654d615,
 author = {Jiang, Puhua and Sun, Mingze and Huang, Ruqi},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {A. Oh and T. Neumann and A. Globerson and K. Saenko and M. Hardt and S. Levine},
 pages = {58409--58427},
 publisher = {Curran Associates, Inc.},
 title = {Non-Rigid Shape Registration via Deep Functional Maps Prior},
 url = {https://proceedings.neurips.cc/paper_files/paper/2023/file/b654d6150630a5ba5df7a55621390daf-Paper-Conference.pdf},
 volume = {36},
 year = {2023}
}

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. For any commercial uses or derivatives, please contact us.

rqhuang88/DFR