Parallel blue-noise sampling by constrained farthest point optimization

Renjie Chen; Craig Gotsman

doi:10.1111/j.1467-8659.2012.03182.x

Parallel blue-noise sampling by constrained farthest point optimization

Research output: Contribution to journal › Article › peer-review

Abstract

We describe a fast sampling algorithm for generating uniformly-distributed point patterns with good blue noise characteristics. The method, based on constrained farthest point optimization, is provably optimal and may be easily parallelized, resulting in an algorithm whose performance/quality tradeoff is superior to other state-of-theart approaches.

Original language	English (US)
Pages (from-to)	1775-1785
Number of pages	11
Journal	Eurographics Symposium on Geometry Processing
Volume	31
Issue number	5
DOIs	https://doi.org/10.1111/j.1467-8659.2012.03182.x
State	Published - 2012
Externally published	Yes

All Science Journal Classification (ASJC) codes

Modeling and Simulation
Geometry and Topology

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10.1111/j.1467-8659.2012.03182.x

Cite this

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title = "Parallel blue-noise sampling by constrained farthest point optimization",

abstract = "We describe a fast sampling algorithm for generating uniformly-distributed point patterns with good blue noise characteristics. The method, based on constrained farthest point optimization, is provably optimal and may be easily parallelized, resulting in an algorithm whose performance/quality tradeoff is superior to other state-of-theart approaches.",

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AB - We describe a fast sampling algorithm for generating uniformly-distributed point patterns with good blue noise characteristics. The method, based on constrained farthest point optimization, is provably optimal and may be easily parallelized, resulting in an algorithm whose performance/quality tradeoff is superior to other state-of-theart approaches.

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Parallel blue-noise sampling by constrained farthest point optimization

Abstract

All Science Journal Classification (ASJC) codes

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