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license: mit
library_name: torch-pointcloud
tags:
- point-cloud
- 3d
- pytorch
- torch-pointcloud
- pointnet2
- segmentation
datasets:
- s3dis
model-index:
- name: pointnet2.s3dis-area5.openpoints
results:
- task:
type: point-cloud-segmentation
dataset:
name: S3DIS (Area 5)
type: s3dis
metrics:
- name: mIoU
type: mean_iou
value: 63.66
---
# Model card for pointnet2.s3dis-area5.openpoints
A PointNet++ point cloud segmentation model (hierarchical set abstraction). Trained on S3DIS (Area 5).
## Model Details
- **Model Type:** Point cloud semantic segmentation
- **Model Stats:**
- Params (M): 1.0
- Input channels: 4
- Classes: 13
- Features: 128
- **Dataset:** S3DIS (Area 5)
- **Metrics:** mIoU 63.66 (reference 63.6)
- **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-area5.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},
}
```
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