Gaussian Blue Noise

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Among the various approaches for producing point distributions with blue noise spectrum, we argue for an optimization framework using Gaussian kernels. We show that with a wise selection of optimization parameters, this approach attains unprecedented quality, provably surpassing the current state of the art attained by the optimal transport (BNOT) approach. Further, we show that our algorithm scales smoothly and feasibly to high dimensions while maintaining the same quality, realizing unprecedented high-quality high-dimensional blue noise sets. Finally, we show an extension to adaptive sampling.
Original languageEnglish (US)
Pages (from-to)1-15
Number of pages15
JournalACM Transactions on Graphics
Volume41
Issue number6
DOIs
StatePublished - Nov 30 2022

Bibliographical note

KAUST Repository Item: Exported on 2022-12-02
Acknowledgements: Thanks to the anonymous reviewers for the valuable comments. Thanks to Mohanad Ahmed for his insightful discussions.

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design

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