Optimal bandwidth selection for MLS surfaces

Hao Wang, Carlos E. Scheidegger, Cĺaudio T. Silva

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

We address the problem of bandwidth selection in MLS surfaces. While the problem has received relatively little attention in the literature, we show that appropriate selection plays a critical role in the quality of reconstructed surfaces. We formulate the MLS polynomial fitting step as a kernel regression problem for both noiseless and noisy data. Based on this framework, we develop fast algorithms to find optimal bandwidths for a large class of weight functions. We show experimental comparisons of our method, which outperforms heuristically chosen functions and weights previously proposed. We conclude with a discussion of the implications of the Levin's two-step MLS projection for bandwidth selection.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Shape Modeling and Applications 2008, Proceedings, SMI
Pages111-120
Number of pages10
DOIs
StatePublished - Sep 9 2008
Externally publishedYes
EventIEEE International Conference on Shape Modeling and Applications 2008, SMI - Stony Brook, NY, United States
Duration: Jun 4 2008Jun 6 2008

Publication series

NameIEEE International Conference on Shape Modeling and Applications 2008, Proceedings, SMI

Other

OtherIEEE International Conference on Shape Modeling and Applications 2008, SMI
CountryUnited States
CityStony Brook, NY
Period6/4/086/6/08

Keywords

  • Bandwidth
  • Kernel regression
  • MLS
  • Point cloud

ASJC Scopus subject areas

  • Computer Science Applications
  • Computational Theory and Mathematics

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