Low-discrepancy blue noise sampling

Abdalla G.M. Ahmed; Hélène Perrier; David Coeurjolly; Victor Ostromoukhov; Jianwei Guo; Dong Ming Yan; Hui Huang; Oliver Deussen

doi:10.1145/2980179.2980218

Low-discrepancy blue noise sampling

Abdalla G.M. Ahmed, Hélène Perrier, David Coeurjolly, Victor Ostromoukhov, Jianwei Guo, Dong Ming Yan, Hui Huang, Oliver Deussen

Visual Computing Center (VCC)

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

35 Scopus citations

Abstract

We present a novel technique that produces two-dimensional low-discrepancy (LD) blue noise point sets for sampling. Using one-dimensional binary van der Corput sequences, we construct two-dimensional LD point sets, and rearrange them to match a target spectral profile while preserving their low discrepancy. We store the rearrangement information in a compact lookup table that can be used to produce arbitrarily large point sets. We evaluate our technique and compare it to the state-of-the-art sampling approaches.

Original language	English (US)
Article number	247
Journal	ACM transactions on graphics
Volume	35
Issue number	6
DOIs	https://doi.org/10.1145/2980179.2980218
State	Published - Nov 2016

Bibliographical note

Funding Information:
We thank the anonymous reviewers for their detailed feedback to improve the paper. Thanks to Jean-Yves Franceschi and Jonathan Dupuy for reviewing an earlier version of the paper. This project was supported in part by Deutsche Forschungsgemeinschaft Grant (DE-620/22-1), French ANR Excellence Chair (ANR-10-CEXC-002-01) and CoMeDiC (ANR-15-CE40-0006), 973 Program (2015CB352501), National Foreign 1000 Plan (WQ201344000169), National Natural Science Foundation of China (61372168, 61620106003, 61331018), GD Leading Talents Plan (00201509), GD Science and Technology Program (2014B050502009, 2014TX01X033, 2015A030312015, 2016A050503036), and SZ Innovation Program (JCYJ20151015151249564).

Publisher Copyright:
© 2016 Copyright held by the owner/author(s). Publication rights licensed to ACM.

Keywords

Blue Noise
Low Discrepancy
Monte Carlo
Quasi-Monte Carlo
Sampling

ASJC Scopus subject areas

Computer Graphics and Computer-Aided Design

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10.1145/2980179.2980218

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abstract = "We present a novel technique that produces two-dimensional low-discrepancy (LD) blue noise point sets for sampling. Using one-dimensional binary van der Corput sequences, we construct two-dimensional LD point sets, and rearrange them to match a target spectral profile while preserving their low discrepancy. We store the rearrangement information in a compact lookup table that can be used to produce arbitrarily large point sets. We evaluate our technique and compare it to the state-of-the-art sampling approaches.",

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author = "Ahmed, {Abdalla G.M.} and H{\'e}l{\`e}ne Perrier and David Coeurjolly and Victor Ostromoukhov and Jianwei Guo and Yan, {Dong Ming} and Hui Huang and Oliver Deussen",

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language = "English (US)",

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AU - Ahmed, Abdalla G.M.

AU - Perrier, Hélène

AU - Coeurjolly, David

AU - Ostromoukhov, Victor

AU - Guo, Jianwei

AU - Yan, Dong Ming

AU - Huang, Hui

AU - Deussen, Oliver

N1 - Funding Information: We thank the anonymous reviewers for their detailed feedback to improve the paper. Thanks to Jean-Yves Franceschi and Jonathan Dupuy for reviewing an earlier version of the paper. This project was supported in part by Deutsche Forschungsgemeinschaft Grant (DE-620/22-1), French ANR Excellence Chair (ANR-10-CEXC-002-01) and CoMeDiC (ANR-15-CE40-0006), 973 Program (2015CB352501), National Foreign 1000 Plan (WQ201344000169), National Natural Science Foundation of China (61372168, 61620106003, 61331018), GD Leading Talents Plan (00201509), GD Science and Technology Program (2014B050502009, 2014TX01X033, 2015A030312015, 2016A050503036), and SZ Innovation Program (JCYJ20151015151249564). Publisher Copyright: © 2016 Copyright held by the owner/author(s). Publication rights licensed to ACM.

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KW - Quasi-Monte Carlo

KW - Sampling

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ER -

Low-discrepancy blue noise sampling

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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