Blue-noise remeshing with farthest point optimization

Dong Ming Yan, Jianwei Guo, Xiaohong Jia, Xiaopeng Zhang, Peter Wonka

Research output: Contribution to journalArticle

15 Scopus citations

Abstract

In this paper, we present a novel method for surface sampling and remeshing with good blue-noise properties. Our approach is based on the farthest point optimization (FPO), a relaxation technique that generates high quality blue-noise point sets in 2D. We propose two important generalizations of the original FPO framework: adaptive sampling and sampling on surfaces. A simple and efficient algorithm for accelerating the FPO framework is also proposed. Experimental results show that the generalized FPO generates point sets with excellent blue-noise properties for adaptive and surface sampling. Furthermore, we demonstrate that our remeshing quality is superior to the current state-of-the art approaches.

Original languageEnglish (US)
Pages (from-to)167-176
Number of pages10
JournalComputer Graphics Forum
Volume33
Issue number5
DOIs
StatePublished - Aug 2014

Keywords

  • Categories and Subject Descriptors (according to ACM CCS)
  • I.3.6 [Computer Graphics]: Methodology and Techniques - Blue-noise sampling and remeshing

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design

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  • Cite this

    Yan, D. M., Guo, J., Jia, X., Zhang, X., & Wonka, P. (2014). Blue-noise remeshing with farthest point optimization. Computer Graphics Forum, 33(5), 167-176. https://doi.org/10.1111/cgf.12442