---
license: mit
library_name: torch-pointcloud
tags:
- point-cloud
- 3d
- pytorch
- torch-pointcloud
- act
- classification
datasets:
- scanobjectnn
base_model: torch-pointcloud/act.pretrain.runpei-dong
model-index:
- name: act.scanobjectnn-objonly.runpei-dong
  results:
  - task:
      type: point-cloud-classification
    dataset:
      name: ScanObjectNN (OBJ_ONLY)
      type: scanobjectnn
    metrics:
    - name: OA
      type: accuracy
      value: 91.22
    - name: mAcc
      type: accuracy
      value: 90.26
---

# Model card for act.scanobjectnn-objonly.runpei-dong

An ACT point cloud classification model (masked point transformer distilled from a 2D image transformer). Trained on ScanObjectNN (OBJ_ONLY).

## Model Details

- **Model Type:** Point cloud classification
- **Model Stats:**
  - Params (M): 22.1
  - Classes: 15
  - Features: 768
- **Dataset:** ScanObjectNN (OBJ_ONLY)
- **Metrics:** OA 91.22, mAcc 90.26
- **Paper:** [Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?](https://arxiv.org/abs/2212.08320)
- **Converted from:** [RunpeiDong/ACT](https://github.com/RunpeiDong/ACT) (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(
    "act.scanobjectnn-objonly.runpei-dong",
    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 = collate([sample])

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{dong2023act,
  title     = {Autoencoders as Cross-Modal Teachers: Can Pretrained {2D} Image Transformers Help {3D} Representation Learning?},
  author    = {Dong, Runpei and Qi, Zekun and Zhang, Linfeng and Zhang, Junbo and Sun, Jianjian and Ge, Zheng and Yi, Li and Ma, Kaisheng},
  booktitle = {ICLR},
  year      = {2023}
}

@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},
}
```