Project Status: Unmaintained. Due to finite time, I have no plans to update this code and I will not be responding to issues.

• Implemention of Pointnet2/Pointnet++ written in PyTorch.
• Supports Multi-GPU via nn.DataParallel.
• Supports PyTorch version >= 1.0.0. Use v1.0 for support of older versions of PyTorch.

See the official code release for the paper (in tensorflow), charlesq34/pointnet2, for official model definitions and hyper-parameters.

The custom ops used by Pointnet++ are currently ONLY supported on the GPU using CUDA.

• Install python -- This repo is tested with {3.6, 3.7}

• Install pytorch with CUDA -- This repo is tested with {1.4, 1.5}. It may work with versions newer than 1.5, but this is not guaranteed.

• Install dependencies

  pip install -r requirements.txt
  

Install with: pip install -e .

There example training script can be found in pointnet2/train.py. The training examples are built using PyTorch Lightning and Hydra.

A classifion pointnet can be trained as

python pointnet2/train.py task=cls
# Or with model=msg for multi-scale grouping
python pointnet2/train.py task=cls model=msg

Similarly, semantic segmentation can be trained by changing the task to semseg

python pointnet2/train.py task=semseg

Multi-GPU training can be enabled by passing a list of GPU ids to use, for instance

python pointnet2/train.py task=cls gpus=[0,1,2,3]

pip install pointnet2_ops_lib/.
# Or if you would like to install them directly (this can also be used in a requirements.txt)
pip install "git+git://github.com/erikwijmans/Pointnet2_PyTorch.git#egg=pointnet2_ops&subdirectory=pointnet2_ops_lib"

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

pip install pre-commit
pre-commit install

@article{pytorchpointnet++,
      Author = {Erik Wijmans},
      Title = {Pointnet++ Pytorch},
      Journal = {https://github.com/erikwijmans/Pointnet2_PyTorch},
      Year = {2018}
}
@inproceedings{qi2017pointnet++,
    title={Pointnet++: Deep hierarchical feature learning on point sets in a metric space},
    author={Qi, Charles Ruizhongtai and Yi, Li and Su, Hao and Guibas, Leonidas J},
    booktitle={Advances in Neural Information Processing Systems},
    pages={5099--5108},
    year={2017}
}