Denoising of Point-clouds Based on Structured Dictionary Learning

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Date
2018
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
We 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.
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@inproceedings{
10.2312:sgp.20181180
, booktitle = {
Symposium on Geometry Processing 2018- Posters
}, editor = {
Ju, Tao and Vaxman, Amir
}, title = {{
Denoising of Point-clouds Based on Structured Dictionary Learning
}}, author = {
Sarkar, Kripasindhu
and
Bernard, Florian
and
Varanasi, Kiran
and
Theobalt, Christian
and
Stricker, Didier
}, year = {
2018
}, publisher = {
The Eurographics Association
}, ISSN = {
1727-8384
}, ISBN = {
978-3-03868-069-7
}, DOI = {
10.2312/sgp.20181180
} }
Citation