/SURFMNet

code and data associated with the paper "Unsupervised Deep Learning for Structured Shape Matching"

Primary LanguagePython

SURFMNet

Source code and data associated with the ICCV'19 oral paper "Unsupervised Deep Learning for Structured Shape Matching". A cleaner and object oriented code is available at https://github.com/Not-IITian/SURFMNet-Object_oriented

Dependency

The code is tested under TF1.6 GPU version and Python 3.6 on Ubuntu 16.04, with CUDA 9.0 and cuDNN 7. It requires Python libraries numpy, scipy.

Download Pre-processed Mesh Data

Please run bash Prepare_data.sh

Shape Matching

To train a DFMnet model to obtain matches between shapes without any ground-truth or geodesic distance matrix (using only a shape's Laplacian eigenvalues and eigenvectors and also Descriptors on shapes):

    python train_DFMnet.py

To obtain matches after training for a given set of shapes:

    python test_DFMnet.py

Visualization of functional maps at each training step is possible with tensorboard:

    tensorboard --logdir=./Training/

Download GT Correspondence and precomputed pairwise matches for some baselines

https://drive.google.com/open?id=1qvqtJz-_zvMxC0ZMuFGbtlKxc9Py3Ggg

Download Geodesic Matrices for Faust and Scape remesh from here:

https://www.dropbox.com/s/ryvc1b0c3gnz2ju/Faust_r_test.zip?dl=0 https://www.dropbox.com/s/ysrctegmqgpo72z/scape_test.zip?dl=0

For any further question, please send an email to Abhishek at kein.iitian@gmail.com.