| 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} | |
| } | |
| ``` | |