Multi-Patch Collaborative Point Cloud Denoising via Low-Rank Recovery with Graph Constraint

Multi-Patch Collaborative Point Cloud Denoising via Low-Rank Recovery with Graph Constraint

Point cloud is the primary source from 3D scanners and depth cameras. It usually contains more raw geometric features, as well as higher levels of noise than the reconstructed mesh. Although many mesh denoising methods have proven to be effective in noise removal, they hardly work well on noisy poin...

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Détails bibliographiques
Publié dans:IEEE transactions on visualization and computer graphics. - 1998. - 26(2020), 11 vom: 01. Nov., Seite 3255-3270
Auteur principal: Chen, Honghua (Auteur)
Autres auteurs: Wei, Mingqiang, Sun, Yangxing, Xie, Xingyu, Wang, Jun
Format: Article en ligne
Langue:English
Publié: 2020
Accès à la collection:IEEE transactions on visualization and computer graphics
Sujets:Journal Article
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245 1 0 |a Multi-Patch Collaborative Point Cloud Denoising via Low-Rank Recovery with Graph Constraint 
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520 |a Point cloud is the primary source from 3D scanners and depth cameras. It usually contains more raw geometric features, as well as higher levels of noise than the reconstructed mesh. Although many mesh denoising methods have proven to be effective in noise removal, they hardly work well on noisy point clouds. We propose a new multi-patch collaborative method for point cloud denoising, which is solved as a low-rank matrix recovery problem. Unlike the traditional single-patch based denoising approaches, our approach is inspired by the geometric statistics which indicate that a number of surface patches sharing approximate geometric properties always exist within a 3D model. Based on this observation, we define a rotation-invariant height-map patch (HMP) for each point by robust Bi-PCA encoding bilaterally filtered normal information, and group its non-local similar patches together. Within each group, all patches are geometrically similar, while suffering from noise. We pack the height maps of each group into an HMP matrix, whose initial rank is high, but can be significantly reduced. We design an improved low-rank recovery model, by imposing a graph constraint to filter noise. Experiments on synthetic and raw datasets demonstrate that our method outperforms state-of-the-art methods in both noise removal and feature preservation 
650 4 |a Journal Article 
700 1 |a Wei, Mingqiang  |e verfasserin  |4 aut 
700 1 |a Sun, Yangxing  |e verfasserin  |4 aut 
700 1 |a Xie, Xingyu  |e verfasserin  |4 aut 
700 1 |a Wang, Jun  |e verfasserin  |4 aut 
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