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.
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.
- Run cosegmentation on the testing dataset, the results would be saved in
/coseg.
python demo_cosegmentation.py
- 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
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}
}