ldmm_graph_laplacian_pointcloud_denoise

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3D Point Cloud Denoising Using Graph Laplacian Regularization of a Low Dimensional Manifold Model

by Jin Zeng, Gene Cheung, Michael Ng, Jiahao Pang, Cheng Yang

To appear on IEEE Trans. on Image Processing

Organization

|--- main_addnoise.m : main for adding noise to gt
|--- main_glr.m : main for GLR denoising
|--- pcdGLR.m : function for GLR denoising
|--- tool : tools for GLR
|--- metric : for computing MSE
|--- setParameter : for parameter setting
|--- 3d_data_set : sample point cloud model "anchor"
	|--- gt : ground truth
	|--- noise : noisy input with noise level 0.02, 0.03, 0.04
|--- anchor : denoising output for "anchor"
|--- README.md : intrustructions

Dependency

The code is tested with MATLAB R2016a.

Demo

1. run main_addnoise.m to get noise corrupted point cloud in ./3d_data_set/noise
2. run main_glr.m to get denoising results in ./anchor

Citation

If our work is useful for your research, please consider citing:

    @inproceedings{zeng20183d,
      title={3d point cloud denoising using graph laplacian regularization of a low dimensional manifold model},
      author={Zeng, Jin and Cheung, Gene and Ng, Michael and Pang, Jiahao and Yang, Cheng},
      booktitle={arXiv preprint arXiv:1803.07252},
      year={2018}
    } 

Contact

Jin Zeng, [email protected]