Maximal poisson-disk sampling via sampling radius optimization

Weize Quan; Dong Ming Yan; Jianwei Guo; Weiliang Meng; Xiaopeng Zhang

doi:10.1145/3005274.3005281

Maximal poisson-disk sampling via sampling radius optimization

Weize Quan, Dong Ming Yan, Jianwei Guo, Weiliang Meng, Xiaopeng Zhang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

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 language	English (US)
Title of host publication	SA 2016 - SIGGRAPH ASIA 2016 Posters
Publisher	Association for Computing Machinery, Inc
ISBN (Electronic)	9781450345408
DOIs	https://doi.org/10.1145/3005274.3005281
State	Published - Nov 28 2016
Externally published	Yes
Event	2016 SIGGRAPH ASIA Posters, SA 2016 - Macau, China Duration: Dec 5 2016 → Dec 8 2016

Publication series

Name	SA 2016 - SIGGRAPH ASIA 2016 Posters

Other

Other	2016 SIGGRAPH ASIA Posters, SA 2016
Country/Territory	China
City	Macau
Period	12/5/16 → 12/8/16

Bibliographical note

Publisher Copyright:
© 2016 Copyright held by the owner/author(s).

Keywords

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

Access to Document

10.1145/3005274.3005281

Cite this

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title = "Maximal poisson-disk sampling via sampling radius optimization",

abstract = "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.",

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AU - Quan, Weize

AU - Yan, Dong Ming

AU - Guo, Jianwei

AU - Meng, Weiliang

AU - Zhang, Xiaopeng

PY - 2016/11/28

Y1 - 2016/11/28

N2 - 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.

AB - 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.

KW - Delaunay triangulation

KW - Maximal Poisson-disk sampling

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M3 - Conference contribution

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Maximal poisson-disk sampling via sampling radius optimization

Abstract

Publication series

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Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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