Point cloud denoising using a generalized error metric

Xu, Qun-Ce, Yang, Yong-Liang and Deng, Bailin ORCID: https://orcid.org/0000-0002-0158-7670 2024. Point cloud denoising using a generalized error metric. Graphical Models 133 , 101216. 10.1016/j.gmod.2024.101216

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Abstract

Effective removal of noises from raw point clouds while preserving geometric features is the key challenge for point cloud denoising. To address this problem, we propose a novel method that jointly optimizes the point positions and normals. To preserve geometric features, our formulation uses a generalized robust error metric to enforce piecewise smoothness of the normal vector field as well as consistency between point positions and normals. By varying the parameter of the error metric, we gradually increase its non-convexity to guide the optimization towards a desirable solution. By combining alternating minimization with a majorization-minimization strategy, we develop a numerical solver for the optimization which guarantees convergence. The effectiveness of our method is demonstrated by extensive comparisons with previous works.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Publisher:	Elsevier
ISSN:	1524-0703
Date of First Compliant Deposit:	11 March 2024
Date of Acceptance:	7 March 2024
Last Modified:	19 Mar 2024 09:51
URI:	https://orca.cardiff.ac.uk/id/eprint/167115

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