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PRODID:Linklings LLC
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TZID:Asia/Seoul
X-LIC-LOCATION:Asia/Seoul
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TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:18871231T000000
DTSTART:19881009T020000
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BEGIN:VEVENT
DTSTAMP:20230103T035312Z
LOCATION:Room 324\, Level 3\, West Wing
DTSTART;TZID=Asia/Seoul:20221209T103000
DTEND;TZID=Asia/Seoul:20221209T120000
UID:siggraphasia_SIGGRAPH Asia 2022_sess173_papers_552@linklings.com
SUMMARY:Gaussian Blue Noise
DESCRIPTION:Technical Communications, Technical Papers\n\nGaussian Blue No
 ise\n\nAhmed, Ren, Wonka\n\nAmong the various approaches for producing poi
 nt distributions with blue noise spectrum, we argue for an optimization fr
 amework using Gaussian kernels.\nWe show that with a wise selection of opt
 imization parameters, this approach attains unprecedented quality, provabl
 y surpassing the current state of the art attained by the optimal transpor
 t (BNOT) approach.\nFurther, we show that our algorithm scales smoothly an
 d feasibly to high dimensions while maintaining the same quality, realizin
 g unprecedented high-quality high-dimensional blue noise sets.\nFinally, w
 e show an extension to adaptive sampling.\n\nRegistration Category: FULL A
 CCESS, ON-DEMAND ACCESS\n\nLanguage: ENGLISH\n\nFormat: IN-PERSON, ON-DEMA
 ND
URL:https://sa2022.siggraph.org/en/full-program/?id=papers_552&sess=sess17
 3
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