Article

Fast Poisson disk sampling in arbitrary dimensions

Author:

Robert BridsonAuthors Info & Claims

SIGGRAPH '07: ACM SIGGRAPH 2007 sketches

Pages 22 - es

https://doi.org/10.1145/1278780.1278807

Published: 05 August 2007 Publication History

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Abstract

In many applications in graphics, particularly rendering, generating samples from a blue noise distribution is important. However, existing efficient techniques do not easily generalize beyond two dimensions. Here I demonstrate a simple modification to dart throwing which permits generation of Poisson disk samples in O(N) time, easily implemented in arbitrary dimension.

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References

[1]

Cook, R. L. 1986. Stochastic sampling in computer graphics. ACM Trans. Graph. 5, 1.

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[2]

Dunbar, D., and Humphreys, G. 2006. A spatial data structure for fast poisson-disk sample generation. ACM Trans. Graph. 25, 3, 503--508.

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Index Terms

Fast Poisson disk sampling in arbitrary dimensions
1. Computing methodologies
  1. Computer graphics

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Published In

SIGGRAPH '07: ACM SIGGRAPH 2007 sketches

August 2007

94 pages

ISBN:9781450347266

DOI:10.1145/1278780

Conference Chairs:
Marc Alexa
Technische Universität Berlin
,
Adam Finkelstein
Princeton University

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 05 August 2007

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SIGGRAPH07

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SIGGRAPH

SIGGRAPH07: Special Interest Group on Computer Graphics and Interactive Techniques Conference

August 5 - 9, 2007

California, San Diego

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Overall Acceptance Rate 1,822 of 8,601 submissions, 21%

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