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SPRIN
qq456cvb/SPRIN
SPRIN is a other model from qq456cvb. Use it for the other task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
Pretrained checkpoints for PRIN/SPRIN: On Extracting Point-wise Rotation Invariant Features (TPAMI 2022), trained on the ShapeNet part segmentation benchmark.
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Public
Updated Jun 12, 2026
Repo size
113 MB
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.pt112 MB · 99%
At a glance
- Task
- other
- License
- mit
- Access
- Public
- Created
- Jun 12, 2026
- Updated
- Jun 12, 2026
- SHA
- 45b0d6a5
Model card
From the Hugging Face model README
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}
}
- Task
- other
- License
- mit
- Created
- Jun 12, 2026
- Updated
- Jun 12, 2026