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Feature-preserving non-local denoising of static and time-varying range data

Published:04 June 2007Publication History

ABSTRACT

We present a new method for noise removal on static and time-varying range data. Our approach predicts the restored position of a perturbed vertex using similar vertices in its neighborhood. It defines the required similarity measure in a new non-local fashion which compares regions of the surface instead of point pairs. This allows our algorithm to obtain a more accurate denoising result than previous state-of-the-art approaches and, at the same time, to better preserve fine features of the surface. Furthermore, our approach is easy to implement, effective, and flexibly applicable to different types of scanned data. We demonstrate this on several static and interesting new time-varying datasets obtained using laser and structured light scanners.

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            cover image ACM Other conferences
            SPM '07: Proceedings of the 2007 ACM symposium on Solid and physical modeling
            June 2007
            455 pages
            ISBN:9781595936660
            DOI:10.1145/1236246

            Copyright © 2007 ACM

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            • Published: 4 June 2007

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