metadata
license: mit
pipeline_tag: other
tags:
- point-cloud
- rotation-invariance
- part-segmentation
- 3d
PRIN/SPRIN Pretrained Weights
Pretrained checkpoints for PRIN/SPRIN: On Extracting Point-wise Rotation Invariant Features (TPAMI 2022), trained on the ShapeNet part segmentation benchmark.
Code: https://github.com/qq456cvb/SPRIN
Contents
| File | Model | Size |
|---|---|---|
epoch250.pt |
SPRIN (sparse PRIN, operates directly on sparse point clouds) | 112 MB |
state79.pkl |
PRIN (Point-wise Rotation Invariant Network with spherical voxel convolution) | 1 MB |
Usage
Download the checkpoints into the corresponding folders of the repository:
hf download qq456cvb/SPRIN epoch250.pt --local-dir sprin
hf download qq456cvb/SPRIN state79.pkl --local-dir prin
Then run the test scripts:
python sprin/test.py # loads sprin/epoch250.pt
python prin/test.py # loads prin/state79.pkl
Citation
@article{you2022prin,
title={PRIN/SPRIN: On Extracting Point-wise Rotation Invariant Features},
author={You, Yang and Lou, Yujing and Shi, Ruoxi and Liu, Qi and Tai, Yu-Wing and Ma, Lizhuang and Wang, Weiming and Lu, Cewu},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume={44},
number={12},
pages={9489--9502},
year={2022},
doi={10.1109/TPAMI.2021.3130590}
}