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PyTorch implementation of Pointnet2/Pointnet++
| Date | Stars |
|---|---|
| 2026-07-24 | 1807 |
| 2026-07-25 | 1807 |
| 2026-07-28 | 1807 |
| 2026-07-30 | 1807 |
| 2026-08-06 | 1807 |
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Pointnet2/Pointnet++ PyTorch
============================
**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 <http://pytorch.org>`_.
* Supports Multi-GPU via `nn.DataParallel <https://pytorch.org/docs/stable/nn.html#torch.nn.DataParallel>`_.
* Supports PyTorch version >= 1.0.0. Use `v1.0 <https://github.com/erikwijmans/Pointnet2_PyTorch/releases/tag/v1.0>`_
for support of older versions of PyTorch.
See the official code release for the paper (in tensorflow), `charlesq34/pointnet2 <https://github.com/charlesq34/pointnet2>`_,
for official model definitions and hyper-parameters.
The custom ops used by Pointnet++ are currently **ONLY** supported on the GPU using CUDA.
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}``.
It may work with versions newer than ``1.5``, but this is not guaranteed.
* Install dependencies
::
pip install -r requirements.txt
Example training
----------------
Install with: ``pip install -e .``
There example training script can be found in ``pointnet2/train.py``. The training examples are built
using `PyTorch Lightning <https://github.com/williamFalcon/pytorch-lightning>`_ and `Hydra <https://hydra.cc/>`_.
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]
Building only the CUDA kernels
----------------------------------
::
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"
Contributing
------------
This repository uses `black <https://github.com/ambv/black>`_ for linting and style enforcement on python code.
For c++/cuda code,
`clang-format <https://clang.llvm.org/docs/ClangFormat.html>`_ is used for style. The simplest way to
comply with style is via `pre-commit <https://pre-commit.com/>`_
::
pip install pre-commit
pre-commit install
Citation
--------
::
@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}
}
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Erik Wijmans
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wassname (Michael J Clark) · I'm just a guy who likes to machine learn · Australia
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Noah Stier · United States
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:b7e33a6b2a9f9470, topic:pytorch