/Easybolic

This is an easy implementation for "Rethinking the compositionality of point clouds through regularization in the hyperbolic space"

Primary LanguagePython

Easybolic

This is an easy implementation for "Rethinking the compositionality of point clouds through regularization in the hyperbolic space"
More details could be found below:
https://github.com/ma-xu/pointMLP-pytorch/tree/main
https://github.com/diegovalsesia/HyCoRe/tree/main

BibTeX

@article{ma2022rethinking,
title={Rethinking network design and local geometry in point cloud: A simple residual MLP framework},
author={Ma, Xu and Qin, Can and You, Haoxuan and Ran, Haoxi and Fu, Yun},
journal={arXiv preprint arXiv:2202.07123},
year={2022}
}

@inproceedings{montanaro2022rethinking,
author = {Montanaro, Antonio and Valsesia, Diego and Magli, Enrico},
booktitle = {Advances in Neural Information Processing Systems},
title = {Rethinking the compositionality of point clouds through regularization in the hyperbolic space},
year = {2022}
}

important

You need to download the pre-trained model of PointMLP!
You need to update your pre-trained model path based on the scenario.