README.md

---
license: mit
library_name: torch-pointcloud
tags:
- point-cloud
- 3d
- pytorch
- torch-pointcloud
- point-transformer-v3
- segmentation
datasets:
- s3dis
model-index:
- name: ptv3-base.s3dis-area5.pointcept
  results:
  - task:
      type: point-cloud-segmentation
    dataset:
      name: S3DIS (Area 5)
      type: s3dis
    metrics:
    - name: mIoU
      type: mean_iou
      value: 32.06
---

# Model card for ptv3-base.s3dis-area5.pointcept

A Point Transformer V3 point cloud segmentation model (serialized neighborhood attention). Trained on S3DIS (Area 5).

## Model Details

- **Model Type:** Point cloud semantic segmentation
- **Model Stats:**
  - Params (M): 46.2
  - Input channels: 6
  - Classes: 13
  - Features: 64
- **Dataset:** S3DIS (Area 5)
- **Metrics:** mIoU 32.06 (reference 73.6)
- **Paper:** [Point Transformer V3: Simpler, Faster, Stronger](https://arxiv.org/abs/2312.10035)
- **Converted from:** [Pointcept/Pointcept](https://github.com/Pointcept/Pointcept) (MIT)
- **Library:** [torch-pointcloud](https://github.com/arthurdjn/pytorch-pointcloud)

## Install

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

This checkpoint also needs `spconv`, which needs a build matching your torch and CUDA: see the [installation guide](https://pytorch-pointcloud.org/installation/).

## Usage

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

model, info = tp.create_model(
    "ptv3-base.s3dis-area5.pointcept",
    task="segmentation",
    pretrained=True,
    return_info=True,
)
model = model.cuda().eval()  # GPU-only kernels

# 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,
    "normal": torch.randn(num_points, 3),
    "segment": torch.zeros(num_points, dtype=torch.long),
    "instance": torch.zeros(num_points, dtype=torch.long),
}
data = info["transform"](sample)
data = collate([data])
data = {key: value.cuda() for key, value in data.items()}

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

## Feature extraction

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

model.reset_classifier(num_classes=0)
with torch.no_grad():
    features = model(data.get("x"), data["pos_grid"], data["batch"], pos=data["pos"])  # (N, 64)
```

## Citation

```bibtex
@inproceedings{wu2024ptv3,
  title   = {Point Transformer V3: Simpler, Faster, Stronger},
  author  = {Xiaoyang Wu and Li Jiang and Peng-Shuai Wang and Zhijian Liu and Xihui Liu and Yu Qiao and Wanli Ouyang and Tong He and Hengshuang Zhao},
  booktitle = {CVPR},
  year    = {2024}
}

@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},
}
```