This is the PyTorch implementation of the ECCV 2022 paper MeshUDF. We provide one dummy pre-trained UDF network and code for demonstrating our differentiable meshing procedure of open surfaces.
The below instructions describe how to:
Set up a conda environment with the right packages using:
conda env create -f conda_env.yml
conda activate meshudf
For speed purposes, our modified version of marching cubes is implemented in Cython. It is largely based on scikit-image implementation of marching cubes Lewiner. To compile the custom version for your system, please run:
cd custom_mc
python setup.py build_ext --inplace
cd ..
The provided UDF network under trained_networks/udf_4_garments/ is an auto-decoder which was trained on 4 different items of garment (0:dress, 1:jeans, 2:sweater, 3:tshirt). In optimize_chamfer_A_to_B.py, we use our method to reconstruct garments associated to latent codes A and B. We then optimize latent code A such that its corresponding mesh is similar to the one of B. This is done by applying a 3D Chamfer loss directly on the meshes, thus demonstrating the end-to-end differentiability of our method.
For example, to launch the reconstruction and optimization from a pair of jeans to a tshirt, run:
python optimize_chamfer_A_to_B.py --experiment trained_networks/udf_4_garments --A 1 --B 3
Feel free to use this code for academic work, but please cite the following:
@inproceedings{guillard2022udf,
author = {Guillard, Benoit and Stella, Federico and Fua, Pascal},
title = {MeshUDF: Fast and Differentiable Meshing of Unsigned Distance Field Networks},
booktitle = {European Conference on Computer Vision},
year = {2022}
}
This code is based on the repos of DeepSDF and scikit-image, whose authors we warmly thank.