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.
Description
@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}
}