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license: mit
library_name: torch-pointcloud
tags:
- point-cloud
- 3d
- pytorch
- torch-pointcloud
- pointnet
- classification
datasets:
- scanobjectnn
model-index:
- name: pointnet.scanobjectnn-hardest.openpoints
results:
- task:
type: point-cloud-classification
dataset:
name: ScanObjectNN (PB_T50_RS)
type: scanobjectnn
metrics:
- name: OA
type: accuracy
value: 75.16
- name: mAcc
type: accuracy
value: 71.44
---
# Model card for pointnet.scanobjectnn-hardest.openpoints
A PointNet point cloud classification model (shared per-point MLP with input and feature transforms). Trained on ScanObjectNN (PB_T50_RS).
## Model Details
- **Model Type:** Point cloud classification
- **Model Stats:**
- Params (M): 3.5
- Input channels: 1
- Classes: 15
- Features: 1024
- **Dataset:** ScanObjectNN (PB_T50_RS)
- **Metrics:** OA 75.16, mAcc 71.44
- **Paper:** [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://arxiv.org/abs/1612.00593)
- **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(
"pointnet.scanobjectnn-hardest.openpoints",
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),
}
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():
embeddings = model.forward_features(data.get("x"), data["pos"], data["batch"])
model.reset_classifier(num_classes=0)
with torch.no_grad():
embeddings = model(data.get("x"), data["pos"], data["batch"]) # (B, 1024)
```
## Citation
```bibtex
@inproceedings{qi2017pointnet,
title = {PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation},
author = {Charles R. Qi and Hao Su and Kaichun Mo and Leonidas J. Guibas},
booktitle = {CVPR},
year = {2017}
}
@inproceedings{uy2019scanobjectnn,
title = {Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data},
author = {Mikaela Angelina Uy and Quang-Hieu Pham and Binh-Son Hua and Duc Thanh Nguyen and Sai-Kit Yeung},
booktitle = {ICCV},
year = {2019}
}
@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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