pytorch-pointcloud

A PyTorch library for deep learning on point clouds: models, pretrained weights, datasets, transforms and inferers, inspired by timm.

Check out the documentation at pytorch-pointcloud.org!

Object classification pointnet2-ssg.modelnet40.xu-yan	Part segmentation pointnext-sm.shapenetpart.openpoints	Indoor segmentation / detection ptv3-base.scannet20.pointcept
Outdoor segmentation / detection spvcnn-119gmacs.semantickitti.mit-han-lab second.kitti.openpcdet	Large scale segmentation utonia-lp.scannet20.pointcept	Feature extraction sonata-lp.scannet20.fair

Installation

Install the library with pip (or uv):

pip install torch-pointcloud

Quickstart

import torch
import torch_pointcloud as tp

# Requires torch-cluster, torch-scatter
model = tp.create_model(
    "pointnext-sm.scanobjectnn-hardest.openpoints",
    task="classification",
    pretrained=True,
).eval()

pos = torch.randn(2048, 3)  # (N, 3) coordinates
x = torch.cat([pos, pos[:, 1:2] - pos[:, 1].min()], dim=1)  # (N, 4) features: xyz + height
batch = torch.zeros(2048, dtype=torch.long)  # (N,) batch index

with torch.no_grad():
    logits = model(x, pos, batch)  # (1, 15)

Each checkpoint ships the transform that turns a raw point cloud into what the network expects:

import torch_pointcloud as tp

# Requires torch-scatter, torch-cluster, spconv
model, info = tp.create_model("ptv3-base.scannet20.pointcept", task="segmentation", pretrained=True, return_info=True)
info["transform"]  # the preprocessing pipeline of that checkpoint
info["weights"]["metrics"]  # {"mIoU": 77.40, "OA": 92.01}

tp.list_models("pointnext*")  # every registered PointNeXt config
tp.list_models(task="detection", pretrained=True)  # all detection checkpoints

See the examples directory for benchmarks and training recipes.

Documentation

The documentation covers installation, a get-started guide, the model zoo, datasets, transforms, tutorials and the full API reference.

Contributing

Contributions are welcome. See CONTRIBUTING.md for the development setup and the pull request checks.

Citation

If you find this project useful, please consider citing:

@software{dujardin2026pytorchpointcloud,
  author = {Dujardin, Arthur},
  title = {PyTorch PointCloud},
  year = {2026},
  url = {https://github.com/arthurdjn/pytorch-pointcloud},
  doi = {10.5281/zenodo.22159632},
  license = {Apache-2.0}
}

License

Apache 2.0. See LICENSE.

Pretrained weights and adapted code keep the license of their source, and some checkpoints are restricted to non-commercial use. Most were trained on research-only datasets, whose terms also apply to the weights. See THIRD_PARTY_NOTICES.md.