Pytorch Implementation of Various Point Transformers

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.


Data Preparation

Download alignment ModelNet here and save in modelnet40_normal_resampled.


Change which method to use in config/config.yaml and run



Using Adam with learning rate decay 0.3 for every 50 epochs, train for 200 epochs; data augmentation follows this repo. For Hengshuang and Nico, initial LR is 1e-3 (maybe fine-tuned later); for Menghao, initial LR is 1e-4, as suggested by the author. ModelNet40 classification results (instance average) are listed below:

Model Accuracy
Hengshuang 89.6
Menghao 92.6
Nico 85.5


Some code and training settings are borrowed from Code for PCT: Point Cloud Transformer (Meng-Hao Guo et al.) is adapted from the author’s Jittor implementation