Recently, various methods applied transformers to point clouds: PCT: Point Cloud Transformer (Meng-Hao Guo et al.), Point Transformer (Nico Engel et al.), Point Transformer (Hengshuang Zhao et al.). This repo is a pytorch implementation for these methods and aims to compare them under a fair setting. Currently, all three methods are implemented, while tuning their hyperparameters.
Prepare data in the same format as 'model net 40'.
install CUDA supported pytorch, and then
pip install -r requirements.txt
Most of the scripts have instructions on usage, so please refer to those when facing any issues.
# Have a data directory
- bash scripts/augment.sh "original_folder" "new_folder"
- original_folder is the directory of your data folder, and new folder is what you want your new directory prefix to be
Change which method to use in config/cls.yaml and run
python train_cls.py
Forked and Modified from [point transformer] (https://github.com/qq456cvb/Point-Transformers). Some code and training settings are borrowed from https://github.com/yanx27/Pointnet_Pointnet2_pytorch. Code for PCT: Point Cloud Transformer (Meng-Hao Guo et al.) is adapted from the author's Jittor implementation https://github.com/MenghaoGuo/PCT.