Denoising of Point-clouds Based on Structured Dictionary Learning

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dc.contributor.authorSarkar, Kripasindhuen_US
dc.contributor.authorBernard, Florianen_US
dc.contributor.authorVaranasi, Kiranen_US
dc.contributor.authorTheobalt, Christianen_US
dc.contributor.authorStricker, Didieren_US
dc.contributor.editorJu, Tao and Vaxman, Amiren_US
dc.date.accessioned2018-07-08T15:27:57Z
dc.date.available2018-07-08T15:27:57Z
dc.date.issued2018
dc.description.abstractWe formulate the problem of point-cloud denoising in terms of a dictionary learning framework over square surface patches. Assuming that many of the local patches (in the unknown noise-free point-cloud) contain redundancies due to surface smoothness and repetition, we estimate a low-dimensional affine subspace that (approximately) explains the extracted noisy patches. This is achieved via a structured low-rank matrix factorization that imposes smoothness on the patch dictionary and sparsity on the coefficients. We show experimentally that our method outperforms existing denoising approaches in various noise scenarios.en_US
dc.description.sectionheadersPosters
dc.description.seriesinformationSymposium on Geometry Processing 2018- Posters
dc.identifier.doi10.2312/sgp.20181180
dc.identifier.isbn978-3-03868-069-7
dc.identifier.issn1727-8384
dc.identifier.pages5-6
dc.identifier.urihttps://doi.org/10.2312/sgp.20181180
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/sgp20181180
dc.publisherThe Eurographics Associationen_US
dc.subjectComputing methodologies
dc.subjectShape analysis
dc.titleDenoising of Point-clouds Based on Structured Dictionary Learningen_US
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