---
license: mit
library_name: torch-pointcloud
tags:
- point-cloud
- 3d
- pytorch
- torch-pointcloud
- pointnet2
- segmentation
datasets:
- s3dis
model-index:
- name: pointnet2.s3dis-area6.openpoints
  results:
  - task:
      type: point-cloud-segmentation
    dataset:
      name: S3DIS (Area 6)
      type: s3dis
    metrics:
    - name: mIoU
      type: mean_iou
      value: 82.45
---

# Model card for pointnet2.s3dis-area6.openpoints

A PointNet++ point cloud segmentation model (hierarchical set abstraction). Trained on S3DIS (Area 6).

## Model Details

- **Model Type:** Point cloud semantic segmentation
- **Model Stats:**
  - Params (M): 1.0
  - Input channels: 4
  - Classes: 13
  - Features: 128
- **Dataset:** S3DIS (Area 6)
- **Metrics:** mIoU 82.45
- **Paper:** [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://arxiv.org/abs/1706.02413)
- **Converted from:** [guochengqian/PointNeXt](https://github.com/guochengqian/PointNeXt) (MIT)
- **Library:** [torch-pointcloud](https://github.com/arthurdjn/pytorch-pointcloud)

## Install

```bash
pip install torch-pointcloud
```

## Usage

```python
import torch
import torch_pointcloud as tp
from torch_pointcloud.utils.data import collate

model, info = tp.create_model(
    "pointnet2.s3dis-area6.openpoints",
    task="segmentation",
    pretrained=True,
    return_info=True,
)
model = model.eval()

# synthetic sample with the keys a dataset provides
num_points = 8192
sample = {
    "pos": torch.randn(num_points, 3),
    "color": torch.rand(num_points, 3) * 255,
}
data = info["transform"](sample)
data = collate([data])

with torch.no_grad():
    logits = model(data.get("x"), data["pos"], data["batch"])
```

## Feature extraction

```python
with torch.no_grad():
    features = model.forward_features(data.get("x"), data["pos"], data["batch"])

model.reset_classifier(num_classes=0)
with torch.no_grad():
    features = model(data.get("x"), data["pos"], data["batch"])  # (N, 128)
```

## Citation

```bibtex
@inproceedings{qi2017pointnet2,
  title   = {PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space},
  author  = {Charles R. Qi and Li Yi and Hao Su and Leonidas J. Guibas},
  booktitle = {NeurIPS},
  year    = {2017}
}

@inproceedings{armeni2016s3dis,
  title     = {{3D} Semantic Parsing of Large-Scale Indoor Spaces},
  author    = {Armeni, Iro and Sener, Ozan and Zamir, Amir R. and Jiang, Helen and Brilakis, Ioannis and Fischer, Martin and Savarese, Silvio},
  booktitle = {CVPR},
  year      = {2016}
}

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