:warning: :rotating_light: this code base is no longer maintained :confused:
A minimalist pytorch implementation of: "Deep Geometric Functional Maps: Robust Feature Learning for Shape Correspondence" [1], appeared in CVPR 2020.
This implementation runs on python >= 3.7, use pip to install dependencies:
pip3 install -r requirements.txt
The preprocessing code will be added later. For the moment, we refer the reader to the original implementation of GeomFmaps to download the data and the preprocessing code.
It should be noted that for each dataset (faust, scape, etc), this module expect that the dataset folder contains 3 folders:
off
folder: this folder contains the meshesspectral
folder: this folder contains the laplace beltrami related data. It's composed from files having the same name as the off
folder. Each fileis a .mat
contaning a dict
containing three keys: evals
, evecs
and evecs_trans
. This files are created by the preprocessing code.corres
folder: this folder contains the ".vts" files necessary for the calculation of the ground truth maps.Use the config.yaml
file to specify the hyperparameters as well as the dataset to be used.
Use the train.py
script to train the GeomFmaps model.
python3 train.py
[1] Deep Geometric Functional Maps: Robust Feature Learning for Shape Correspondence