Point-Transformer PyTorch

Implemention of Point Transformer.
Code based on PointNet2 Pytorch repository.

Setup

Install python -- This repo is tested with {3.6, 3.7}
Install pytorch with CUDA -- This repo is tested with {1.4, 1.5, 1.7.1}. It may work with versions newer than 1.7.1, but this is not guaranteed.
Install dependencies
```
pip install -r requirements.txt
```

Training

Install with: pip install -e .

The example training script can be found in point_transformer/train.py. The training examples are built using PyTorch Lightning and Hydra.

You can train a Point Transformer model on various tasks as,

# train Point Transformer for classification task on ModelNet40
python -m point_transformer.train task=cls

# train Point Transformer for part segmentation task on ShapeNet
python -m point_transformer.train task=partseg

# train Point Transformer for semantic segmentation task on S3DIS
python -m point_transformer.train task=semseg

If you want to override the default config, you can pass the command line arguments,

# Change the batch size to 32
python -m point_transformer.train task=cls batch_size=32

Building only the CUDA kernels

pip install point_transformer_lib/.

Experiment Results

Classification on ModelNet40

Model	mAcc	OA
Paper Our Implemention	90.6	93.7 87.2

Part Segmentation on ShapeNet

Model	cat. mIoU	ins. mIoU
Paper Our Implemention	83.7	86.6 83.2

Semantic Segmentation on S3DIS Area5

Model	mAcc	OA	mIoU
Paper Our Implemention	76.5	90.8 88.0	70.4

Contributing

This repository uses black for linting and style enforcement on python code. For c++/cuda code, clang-format is used for style. The simplest way to comply with style is via pre-commit