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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},
}
```
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