Sarkar, KripasindhuBernard, FlorianVaranasi, KiranTheobalt, ChristianStricker, DidierJu, Tao and Vaxman, Amir2018-07-082018-07-082018978-3-03868-069-71727-8384https://doi.org/10.2312/sgp.20181180https://diglib.eg.org:443/handle/10.2312/sgp20181180We 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.Computing methodologiesShape analysis10.2312/sgp.201811805-6