---
license: mit
library_name: torch-pointcloud
tags:
- point-cloud
- 3d
- pytorch
- torch-pointcloud
- votenet
- object-detection
datasets:
- scannet
model-index:
- name: votenet.scannet.fair
  results:
  - task:
      type: 3d-object-detection
    dataset:
      name: ScanNet
      type: scannet
    metrics:
    - name: mAP@0.25
      type: map
      value: 57.65
    - name: mAP@0.5
      type: map
      value: 34.1
---

# Model card for votenet.scannet.fair

A VoteNet 3D object detection model (deep Hough voting detector). Trained on ScanNet.

## Model Details

- **Model Type:** 3D object detection
- **Model Stats:**
  - Params (M): 1.0
  - Input channels: 1
  - Classes: 18
  - Features: 256
- **Dataset:** ScanNet
- **Metrics:** mAP@0.25 57.65, mAP@0.5 34.1 (reference 58.6)
- **Paper:** [Deep Hough Voting for 3D Object Detection in Point Clouds](https://arxiv.org/abs/1904.09664)
- **Converted from:** [facebookresearch/votenet](https://github.com/facebookresearch/votenet) (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(
    "votenet.scannet.fair",
    task="detection",
    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),
    "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])

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

## Feature extraction

```python
with torch.no_grad():
    features = model.forward_features(data.get("x"), data["pos"], data["batch"])  # 256 channels
```

## Citation

```bibtex
@inproceedings{qi2019votenet,
  title   = {Deep Hough Voting for 3D Object Detection in Point Clouds},
  author  = {Charles R. Qi and Or Litany and Kaiming He and Leonidas J. Guibas},
  booktitle = {ICCV},
  year    = {2019}
}

@inproceedings{dai2017scannet,
  title   = {ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes},
  author  = {Angela Dai and Angel X. Chang and Manolis Savva and Maciej Halber and Thomas Funkhouser and Matthias Nießner},
  booktitle = {CVPR},
  year    = {2017}
}

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