pvnieo / GeomFmaps_pytorch

A minimalist pytorch implementation of: "Deep Geometric Functional Maps: Robust Feature Learning for Shape Correspondence"
MIT License
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feature-extraction functional-maps python3 pytorch shape-correspondence shape-descriptor shape-matching

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:warning: :rotating_light: this code base is no longer maintained :confused:

GeomFmaps-pytorch

A minimalist pytorch implementation of: "Deep Geometric Functional Maps: Robust Feature Learning for Shape Correspondence" [1], appeared in CVPR 2020.

Installation

This implementation runs on python >= 3.7, use pip to install dependencies:

pip3 install -r requirements.txt

Download data & preprocessing

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:

Usage

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

References

[1] Deep Geometric Functional Maps: Robust Feature Learning for Shape Correspondence