---
license: mit
library_name: torch-pointcloud
tags:
- point-cloud
- 3d
- pytorch
- torch-pointcloud
- pointnext
- segmentation
datasets:
- shapenetpart
model-index:
- name: pointnext-sm.shapenetpart.openpoints
  results:
  - task:
      type: point-cloud-segmentation
    dataset:
      name: ShapeNetPart
      type: shapenetpart
    metrics:
    - name: ins_mIoU
      type: mean_iou
      value: 86.88
    - name: cls_mIoU
      type: mean_iou
      value: 84.48
---

# Model card for pointnext-sm.shapenetpart.openpoints

A PointNeXt point cloud segmentation model (scaled PointNet++ with inverted residual blocks). Trained on ShapeNetPart.

## Model Details

- **Model Type:** Point cloud semantic segmentation
- **Model Stats:**
  - Params (M): 1.0
  - Input channels: 7
  - Classes: 50
  - Features: 96
- **Dataset:** ShapeNetPart
- **Metrics:** ins_mIoU 86.88, cls_mIoU 84.48 (reference 86.7)
- **Paper:** [PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies](https://arxiv.org/abs/2206.04670)
- **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(
    "pointnext-sm.shapenetpart.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),
    "normal": torch.randn(num_points, 3),
    "category": torch.tensor(0),
    "segment": torch.zeros(num_points, dtype=torch.long),
}
data = info["transform"](sample)
data = collate([data])

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

## 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"], data["category"])  # (N, 96)
```

## Citation

```bibtex
@inproceedings{qian2022pointnext,
  title   = {PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies},
  author  = {Guocheng Qian and Yuchen Li and Houwen Peng and Jinjie Mai and Hasan Abed Al Kader Hammoud and Mohamed Elhoseiny and Bernard Ghanem},
  booktitle = {NeurIPS},
  year    = {2022}
}

@article{yi2016shapenetpart,
  title   = {A Scalable Active Framework for Region Annotation in {3D} Shape Collections},
  author  = {Yi, Li and Kim, Vladimir G. and Ceylan, Duygu and Shen, I-Chao and Yan, Mengyan and Su, Hao and Lu, Cewu and Huang, Qixing and Sheffer, Alla and Guibas, Leonidas},
  journal = {ACM Transactions on Graphics (TOG)},
  volume  = {35},
  number  = {6},
  year    = {2016}
}

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