| ---
|
| license: mit
|
| pipeline_tag: other
|
| tags:
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| - point-cloud
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| - rotation-invariance
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| - part-segmentation
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| - 3d
|
| ---
|
|
|
| # PRIN/SPRIN Pretrained Weights
|
|
|
| Pretrained checkpoints for [PRIN/SPRIN: On Extracting Point-wise Rotation Invariant Features](https://doi.org/10.1109/TPAMI.2021.3130590) (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:
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|
|
| ```bash
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| hf download qq456cvb/SPRIN epoch250.pt --local-dir sprin
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| hf download qq456cvb/SPRIN state79.pkl --local-dir prin
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| ```
|
|
|
| Then run the test scripts:
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|
|
| ```bash
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| python sprin/test.py # loads sprin/epoch250.pt
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| python prin/test.py # loads prin/state79.pkl
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| ```
|
|
|
| ## Citation
|
|
|
| ```bibtex
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| @article{you2022prin,
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| title={PRIN/SPRIN: On Extracting Point-wise Rotation Invariant Features},
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| 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},
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| journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
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| volume={44},
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| number={12},
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| pages={9489--9502},
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| year={2022},
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| doi={10.1109/TPAMI.2021.3130590}
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| }
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| ```
|
|
|
