Deep Functional Maps

This page contains a TensorFlow implementation (version 1.3.0) of the method described in https://arxiv.org/pdf/1704.08686

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Instructions

Download Data and Results folders from: https://drive.google.com/drive/folders/1nHUGaKcn2INwXln6Ig354y6o9WI-_Kzj?usp=sharing
To run on a pair of shapes, update their filenames in test_pairs.txt
run test_FMnet.py

Data description

The network recieves as input a pair of shapes, in the format of a mat struct with precomputed shot descriptors and Laplacian eigenfunctions. See the example shapes provided in './Data/'.

Data pre-processing

Faust models are scaled by a factor of 100. To compute SHOT descriptors, the calc_shot function was used (see Utils folder) with the following parameters: num_bins = 10, radius = 9, min_neighs = 3: calc_shot([model.X model.Y model.Z]', model.TRIV', 1:numel(model.X), num_bins, radius, min_neighs)';

Pre-trained models

Currently we only provide a model trained for a small number of iterations (~1200) on the registered faust models. We will do our best to update this. Note that these are not the parameters used to produce the results published in the paper.

TODO

Updtae pre-trained models
Add postprocessing as done in the paper