First, you may download the ModelNet40 dataset from here, and place it to cls/data/modelnet40_ply_hdf5_2048. We use the prepared data in HDF5 files for principle evaluation, where each object is already sampled to 2048 points. The experiments presented in the paper uses 1024 points for training and testing.
To train a model for classification:
python train.py
Model and log files will be saved to cls/models/train/ in default. After the training stage, you can test the model by:
python train.py --eval 1
If you'd like to use your own data, you can modify data.py to change the data-loading path.