Denoising of point-clouds based on structured dictionary learning

Denoising of point-clouds based on structured dictionary learning
Kripasindhu Sarkar, Florian Bernard, Kiran Varanasi, Christian Theobalt, Didier Stricker
Eurographics Symposium on Geometry Processing (SGP-2018), July 7-11, Paris, France

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.