PRIN

Pointwise Rotation-Invariant Network in PyTorch

Overview

This repository is the Pytorch implementation of PRIN (Pointwise Rotation-Invariant Network).

Dependencies

• Install s2cnn (https://github.com/jonas-koehler/s2cnn) and its dependencies (pytorch, cupy, lie_learn, pynvrtc).
• Install pybind11 and compile the script under src (https://pybind11.readthedocs.io/)

Dataset and pretrained weights

• Download ShapeNet 17 Part Segmentation Dataset in h5py format from https://drive.google.com/drive/folders/1wC-DpeRtxuuEvffubWdhwoGXGeW052Vy?usp=sharing
• Download pretrained weights (trained on unrotated shapes) from https://drive.google.com/open?id=1QnFqQdWmx0cYtYeN9tJNlf-E5ZLawRBv

Usage

• For training, run "python train.py --log_dir log --model_path ./model.py --num_workers 4"
• For testing, run "python test.py --weight_path ./state.pkl --model_path ./model.py --num_workers 4"

License

MIT

References

Our paper is available on https://arxiv.org/abs/1811.09361.

Citation

@article{prin,
  title={PRIN: Pointwise Rotation-Invariant Network},
  author={Yang You and Yujing Lou and Qi Liu and Yu-Wing Tai and Weiming Wang and Lizhuang Ma and Cewu Lu},
  journal={arXiv preprint arXiv:1811.09361},
  year={2018}
}