---
license: mit
tags:
- point-cloud
- rotation-invariance
- part-segmentation
- pytorch
library_name: pytorch
---

# PRIN: Pointwise Rotation-Invariant Network (AAAI 2020) — Pretrained Weights

Pretrained PyTorch weights (`state.pkl`) for **PRIN**, from:

> [Pointwise Rotation-Invariant Network with Adaptive Sampling and 3D Spherical Voxel Convolution (AAAI 2020)](https://ojs.aaai.org/index.php/AAAI/article/view/6965)

Code and usage instructions: https://github.com/qq456cvb/PRIN

The model is trained on the ShapeNet 17-category part segmentation dataset (unrotated shapes).

## Usage

```bash
hf download qq456cvb/PRIN state.pkl --local-dir .
python test.py --weight_path ./state.pkl --model_path ./model.py --num_workers 4
```

## Citation

```bibtex
@inproceedings{you2020pointwise,
  title={Pointwise Rotation-Invariant Network with Adaptive Sampling and 3D Spherical Voxel Convolution},
  author={You, Yang and Lou, Yujing and Liu, Qi and Tai, Yu-Wing and Ma, Lizhuang and Lu, Cewu and Wang, Weiming},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={34},
  number={07},
  pages={12717--12724},
  year={2020}
}
```