The code changes based on the GCNN. In Extract_shape_descriptors folder, there are files used to compute multiple shape descriptors Be careful of the folder/file path 1: run run_forrest_run.m 2: In data_process.m to compute hks 3: In crop_data.m, it connects multiple shape descriptors. If it is identical class, you only need to run the final part.(Concate desc data) If it is multiple classes, you need to run all parts. For the (Cut geovec) part, you need run it for several times to crop each shape descriptor, then in Concate desc data part, you need to run it to connect each shape descriptor one by one. In the Network_training folder, there are files used to train the model Be careful of the folder/file path In \Experiment folder, it it main python file to run experiment experiment1: the whole model experiment2: improvement comparison experiment2_1: the model without multiple shape descriptors experiment2_2: the model without concatenating layer experiment3: architecture comparison experiment3_1: the model with input linear layer experiment3_2: the model with adjusted linear layer experiment3_3: the model with 2 combined GC layer experiment3_4: the model with 1 combined GC layer experiment4: parameter setting comparison experiment4_1: test epoch = 100; train epoch = 100 experiment4_2: test epoch = 50; train epoch = 100 experiment4_2: test epoch = 50; train epoch = 50 experiment5: method comparison in multiple class change the train.txt and test.txt to evaluate the result In the command.txt is the command to run the python file Steps to run the code: 1. Put experiment file you want to run into the network_training folder 2. Run the command line in the command.txt All other experiment files should be run following above steps, be careful to change the data path according to the Example.py
stwang1994/shape_correspondence
Novel deep learning architecture that has state-of-the-art performance in shape correspondence, which is applied in 3D reconstruction and retrieval
MATLAB