Blue-noise point sampling using kernel density model

Raanan Fattal*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

66 Scopus citations

Abstract

Stochastic point distributions with blue-noise spectrum are used extensively in computer graphics for various applications such as avoiding aliasing artifacts in ray tracing, halftoning, stippling, etc. In this paper we present a new approach for generating point sets with high-quality blue noise properties that formulates the problem using a statistical mechanics interacting particle model. Points distributions are generated by sampling this model. This new formulation of the problem unifies randomness with the requirement for equidistant point spacing, responsible for the enhanced blue noise spectral properties. We derive a highly efficient multi-scale sampling scheme for drawing random point distributions from this model. The new scheme avoids the critical slowing down phenomena that plagues this type of models. This derivation is accompanied by a model-specific analysis. Altogether, our approach generates high-quality point distributions, supports spatially-varying spatial point density, and runs in time that is linear in the number of points generated.

Original languageAmerican English
Title of host publicationProceedings of ACM SIGGRAPH 2011, SIGGRAPH 2011
Volume30
Edition4
DOIs
StatePublished - Jul 2011
EventACM SIGGRAPH 2011, SIGGRAPH 2011 - Vancouver, BC, Canada
Duration: 7 Aug 201111 Aug 2011

Conference

ConferenceACM SIGGRAPH 2011, SIGGRAPH 2011
Country/TerritoryCanada
CityVancouver, BC
Period7/08/1111/08/11

Keywords

  • And anti-aliasing
  • Blue noise
  • Image synthesis
  • Importance sampling
  • Poisson disk distribution
  • Stochastic sampling

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