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license: mit
library_name: torch-pointcloud
tags:
- point-cloud
- 3d
- pytorch
- torch-pointcloud
- point-bert
- classification
datasets:
- modelnet40
base_model: torch-pointcloud/point-bert-base.pretrain.xumin-yu
model-index:
- name: point-bert-base.modelnet40.xumin-yu
results:
- task:
type: point-cloud-classification
dataset:
name: ModelNet40
type: modelnet40
metrics:
- name: OA
type: accuracy
value: 92.63
---
# Model card for point-bert-base.modelnet40.xumin-yu
A Point-BERT point cloud classification model (masked point modeling transformer). Trained on ModelNet40.
## Model Details
- **Model Type:** Point cloud classification
- **Model Stats:**
- Params (M): 22.1
- Classes: 40
- Features: 768
- **Dataset:** ModelNet40
- **Metrics:** OA 92.63 (reference 92.67)
- **Paper:** [Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling](https://arxiv.org/abs/2111.14819)
- **Converted from:** [Julie-tang00/Point-BERT](https://github.com/Julie-tang00/Point-BERT) (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(
"point-bert-base.modelnet40.xumin-yu",
task="classification",
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),
}
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, 768)
```
## Citation
```bibtex
@inproceedings{yu2022pointbert,
title = {Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling},
author = {Xumin Yu and Lulu Tang and Yongming Rao and Tiejun Huang and Jie Zhou and Jiwen Lu},
booktitle = {CVPR},
year = {2022}
}
@inproceedings{wu2015modelnet,
title = {3D ShapeNets: A Deep Representation for Volumetric Shapes},
author = {Zhirong Wu and Shuran Song and Aditya Khosla and Fisher Yu and Linguang Zhang and Xiaoou Tang and Jianxiong Xiao},
booktitle = {CVPR},
year = {2015}
}
@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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