Maximal poisson-disk sampling via sampling radius optimization

Weize Quan, Dongming Yan, Jianwei Guo, Weiliang Meng, Xiaopeng Zhang

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

4 Scopus citations


Maximal Poisson-disk Sampling (MPS) is a fundamental research topic in computer graphics. An ideal MPS pattern should satisfy three properties: bias-free, minimal distance, maximal coverage. The classic approach for generating MPS is dart throwing, but this method is unable to precisely control the number of samples when achieving maximality [Ebeida et al. 2011]. Sample elimination [Yuksel 2015] is an recently proposed algorithm that could generate Poisson-disk sets with an exactly desired size, but it cannot guarantee the maximal coverage. In this work, we propose a simple 2D MPS algorithm that can precisely control the number of samples, while meeting all three criteria simultaneously. Unlike previous conflict-based methods, our algorithm controls the number of samples by dynamically adjusting sampling radius.

Original languageEnglish (US)
Title of host publicationSA 2016 - SIGGRAPH ASIA 2016 Posters
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450345408
StatePublished - Nov 28 2016
Event2016 SIGGRAPH ASIA Posters, SA 2016 - Macau, China
Duration: Dec 5 2016Dec 8 2016

Publication series

NameSA 2016 - SIGGRAPH ASIA 2016 Posters


Other2016 SIGGRAPH ASIA Posters, SA 2016


  • Delaunay triangulation
  • Maximal Poisson-disk sampling

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction


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