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DTSTART:18871231T000000
DTSTART:19881009T020000
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BEGIN:VEVENT
DTSTAMP:20230103T035307Z
LOCATION:Auditorium\, Level 5\, West Wing
DTSTART;TZID=Asia/Seoul:20221206T100000
DTEND;TZID=Asia/Seoul:20221206T120000
UID:siggraphasia_SIGGRAPH Asia 2022_sess153_papers_354@linklings.com
SUMMARY:Scalable multi-class sampling via filtered sliced optimal transpor
 t
DESCRIPTION:Technical Papers\n\nScalable multi-class sampling via filtered
  sliced optimal transport\n\nSALAUN, Geogiev, Seidel, Singh\n\nWe propose 
 a continuous domain formulation of Wasserstein barycenters\nfor multi-clas
 s (-purpose) point set optimization. Our formulation is sys-\ntematically 
 derived to handle hundreds to thousands of classes for different\nsampling
  applications. We develop a practical optimization scheme that is\nclosely
  paired with our formulation. To demonstrate the generalizability of\nour 
 framework beyond sampling, we formalize the problem of blue-noise\nerror d
 istribution as a multi-class problem. This helps establish a direct\nconne
 ction between sampling, reconstruction and perceptual filtering in\nrender
 ing. The resulting formulation provide error bounds on the perceptual\nerr
 or which, when optimized for, gives screen space blue-noise error dis-\ntr
 ibution. We demonstrate the effectiveness of our framework on different\ns
 ampling applications like stippling, object placement and rendering.\n\nRe
 gistration Category: FULL ACCESS, EXPERIENCE PLUS ACCESS, EXPERIENCE ACCES
 S, TRADE EXHIBITOR\n\nLanguage: ENGLISH\n\nFormat: IN-PERSON
URL:https://sa2022.siggraph.org/en/full-program/?id=papers_354&sess=sess15
 3
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