This code implements a deep learning method on Local Point Signature for 3D surface shape matching described in our CVPR 2019 paper:

"A Robust Local Spectral Descriptor for Matching Non-Rigid Shapes with Incompatible Shape Structures"

by Yiqun Wang, Jianwei Guo, Dong-Ming Yan, Kai Wang, Xiaopeng Zhang.

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Please consider citing the above paper if this code/program (or part of it) benefits your project.

There are three folders here. The "cpp" calls the "matlab" for GI generation. The "python" is used for network training and testing.

The usage is as follows:

1. Compile matlab project:

    MCC matlab "mcc -W cpplib:libcompcur -T link:lib compute_curvature.m". We got the libcompcur.dll that is going to be added to the CPP project.
   

2. Build cpp solution: this code is to generate geometry images. You can run this step in your local desktop.

    Modify CMakeLists: 
    	add include_directories and link_directories for openmesh and matlab runtime
    Cmake
    Build solution
    Modify config.ini for mesh_dir(directory of OFF models) gi_dir(directory of geometry images) and kpi_dir(directory of key points, you can skip it for dense matching)
    	edit other paras such as gi_size(NxN of gi), rotation_num and radius_list_p(the ratio of geodesic diameter).
    Add "libcompcur.dll" to folder with GIGen.exe
    Run "GIGen.exe config.ini" to generate GI
   

3. Python project: this code is to train and test network. You should copy the geometry images generated in last step into the server.

   3.1 Train network:

    run "classify_gi_by_pidx_and_split.py" to classify dataset by point index for generating Tfrecord
    	source_dir is the folder of geometry images(rotation_num=12) for training, destination_dir is the generated folder after splitting
    run "tfr_gen.py" to generate Tfrecord
    	gi_dir is the same as destination_dir, tfr_dir is the generated folder of Tfrecord.
    run "train_softmax6890.py" to pretrain a classification network
    	tfr_dir needs to be specified
    run "train_mincv_perloss6890_256.py" to train the triplet network by restoring a pre-trained classification model
    	Tfr_dir needs to be specified
   

   3.2 Test to generate descriptor:

    run "descGen.py" to generate descriptor using test dataset
    	gi_dir is the folder of geometry images(rotation_num=1) for testing, desc_dir is the generated folder of descriptor.
   

This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version.