Blue-noise remeshing with farthest point optimization

Dongming Yan, Jianwei Guo, Xiaohong Jia, Xiaopeng Zhang, Peter Wonka

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


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. © 2014 The Eurographics Association and John Wiley & Sons Ltd.
Original languageEnglish (US)
Pages (from-to)167-176
Number of pages10
JournalComputer Graphics Forum
Issue number5
StatePublished - Aug 23 2014

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: We are grateful to anonymous reviewers for their suggestive comments. We would like to thank Liyi Wei and Rui Wang for sharing the DDA tool, Zhonggui Chen, Ligang Liu and Esdras Medeiros for providing us with their results for comparison. This work was partially supported by the KAUST Visual Computing Center, the National Natural Science Foundation of China (nos. 61372168, 61331018, 61271431, 11201463), and the U.S. National Science Foundation.

ASJC Scopus subject areas

  • Computer Networks and Communications


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