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Low Rank Matrix Approximation for 3D Geometry Filtering, IEEE TVCG 2020

How to use:

(1) install Matlab (tested successfully on Matlab 2021a, 2015 etc)

(2) for point cloud data, run “Implementation_lowrankpaper_pointcloud.m” in the folder of low-rank-pointcloud

(3) for mesh data, run “Implementation_lowrankpaper_mesh.m” in the folder of low-rank_mesh. Note: the released code is only the basic implementation, which might have slight difference in filtering/denosing results. The code is for research purpose only.

Please cite our paper:

@ARTICLE{Lu2020lowrank,
author={Lu, Xuequan and Schaefer, Scott and Luo, Jun and Ma, Lizhuang and He, Ying},
journal={IEEE Transactions on Visualization and Computer Graphics},
title={Low Rank Matrix Approximation for 3D Geometry Filtering},
year={2020},
volume={},
number={},
pages={1-1},
doi={10.1109/TVCG.2020.3026785}}

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  • MATLAB 100.0%