Novel deep learning architecture that has state-of-the-art performance in shape correspondence, which is applied in 3D reconstruction and retrieval
MATLAB 53.64%M 0.21%C 1.99%Python 44.16%
shape_correspondence's Introduction
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