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AdaCoSeg

This is an Pytorch demo of the paper "AdaCoSeg: Adaptive Shape Co-Segmentation with Group Consistency Loss". This is a deep neural network architecture for adaptive co-segmentation of a set of 3D shapes represented as point clouds.

Usage

Dependancy

This implementation should be run with Python 3.x and Pytorch >= 0.4.0.

We provide a demo dataset for training and testing, you can download it from this "link". Put the unziped files in the folder /chair.

Demo

You can train your own offline network through:

python trainOffline.py

Or, you can use our pretrained model /chair/PartSpace_Training.pkl to run two interesting demo.

  1. Run cosegmentation on the testing dataset, the results would be saved in /coseg.
python demo_cosegmentation.py
  1. Run cosegmentation on the training dataset, the results would be saved in /refineTraining. You can compare the segmentation consistency before and after the cosegmentation.
python demo_refineTrainingData.py 

Citation

If you use this code, please cite the following paper.

@misc{zhu2019adacoseg,
    title={AdaCoSeg: Adaptive Shape Co-Segmentation with Group Consistency Loss},
    author={Chenyang Zhu and Kai Xu and Siddhartha Chaudhuri and Li Yi and Leonidas Guibas and Hao Zhang},
    year={2019},
    eprint={1903.10297},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

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