The project runs on a framework of Caffe and Python. The caffe library can be downloaded from https://github.com/BVLC/caffe. Follow the instructios in the repository to install dependencies for Caffe.
The data files consist of preprocessed 30x30x30 voxel grids from the ModelNet10 database. (http://modelnet.cs.princeton.edu/) First download the following files to the data/ folder https://drive.google.com/open?id=0B7cRYqpXc0QJMUx1VE0xSVM2MU0 (partial_view.data) https://drive.google.com/open?id=0B7cRYqpXc0QJaVJGYUpzcDJGdkk (complete_view.data) https://drive.google.com/open?id=0B7cRYqpXc0QJNGxTS3JhSkFWX1E (labels.data)
To train sample completion on all 10 classes run:
python sample_completion.py architecture/1200/Net3DReg_solver.prototxt
To train sample completion with output mask run:
python sample_completion_difference.py architecture/1200-difference/Net3DReg_solver.prototxt
The trained network is saved to the results folder in the form of caffemodel and solverstate files.
The test error for a specified architecture can be obtained by:
python get_test_error.py architecture/1200/Net3DReg_solver.prototxt results/1200/_iter_100000.caffemodel
Completed views for objects can be generated for a specified architecture by:
python generate_completion.py architecture/1200/Net3DReg_solver.prototxt results/1200/_iter_100000.caffemodel results/single/1200/_iter_100000.caffemodel