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

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.

Please run bash Prepare_data.sh

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/

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

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 [email protected].