SPU-Net: Self-Supervised Point Cloud Upsampling by Coarse-to-Fine Reconstruction with Self-Projection Optimization
Created by Xinhai Liu, Zhizhong Han, Yu-Shen Liu, Matthias Zwicker.
For training, the cmd example:
python train_spu-net.py --gpu gpu_index --log_dir log/spu_00012_git
For evaluating, the cmd example:
python evaluate.py --gt data/test/groundtruth/ --pred log/spu_00012_git/output/
The training data from PU-GAN:
path to the test point cloud and ground-truths: data/test
You can download the entire dataset from PU-GAN or Google Drive.
If you find our work useful in your research, please consider citing:
@article{liu2022spu,
title={SPU-Net: Self-Supervised Point Cloud Upsampling by Coarse-to-Fine Reconstruction with Self-Projection Optimization},
author={Liu, Xinhai and Liu, Xinchen and Liu, Yu-Shen and Han, Zhizhong},
journal={IEEE Transactions on Image Processing},
volume={31},
pages={4213--4226},
year={2022},
publisher={IEEE}
}
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