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Noise analysis and synthesis for 3D laser depth scanners

Sun, Xianfang ORCID:, Rosin, Paul L. ORCID:, Martin, Ralph Robert and Langbein, Frank Curd ORCID: 2009. Noise analysis and synthesis for 3D laser depth scanners. Graphical Models 71 (2) , pp. 34-48. 10.1016/j.gmod.2008.12.002

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This paper analyses the noise present in range data measured by a Konica Minolta Vivid 910 scanner, in order to better characterise real scanner noise. Methods for denoising 3D mesh data have often assumed the noise to be Gaussian, and independently distributed at each mesh point. We show via measurements of an accurately machined almost planar test surface that real scanner data does not have such properties: the errors are not quite Gaussian, and more importantly, exhibit significant short range correlation. We use this to give a simple model for generating noise with similar characteristics. We also consider how noise varies with such factors as laser intensity, orientation of the surface, and distance from the scanner. Finally, we evaluate the performance of three typical mesh denoising algorithms using real and synthetic test data, and suggest that new denoising algorithms are required for effective removal of real noise.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA76 Computer software
Uncontrolled Keywords: 3D laser scanner; scanner noise analysis; noise modeling; 3D surface denoising
Additional Information: PDF uploaded in accordance with publisher's policy [accessed 30/03/2015] NOTICE: this is the author’s version of a work that was accepted for publication in Graphical Models. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Graphical Models, [VOL 71, ISSUE 2, 2009 DOI 10.1016/j.gmod.2008.12.002
Publisher: Elsevier
ISSN: 1524-0703
Last Modified: 06 Nov 2023 22:52

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