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TZID:Asia/Seoul
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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_361@linklings.com
SUMMARY:MIPNet: Neural Normal-to-Anisotropic-Roughness MIP mapping
DESCRIPTION:Technical Papers\n\nMIPNet: Neural Normal-to-Anisotropic-Rough
 ness MIP mapping\n\nGauthier, Faury, Levallois, Thonat, Thiery...\n\nWe pr
 esent MIPNet, a novel approach for SVBRDF mipmapping which preserves mater
 ial appearance under varying view distances and lighting conditions. As in
  classical mipmapping, our method explicitly encodes the multiscale appear
 ance of materials in a SVBRDF mipmap pyramid. To do so, we use a tensor-ba
 sed representation, coping with gradient-based optimization, for encoding 
 anisotropy which is compatible with existing real-time rendering engines. 
 Instead of relying on a simple texture patch average for each channel inde
 pendently, we propose a cascaded architecture of multilayer perceptrons to
  approximate the material appearance using only the fixed material channel
 s. Our neural model learns simple mipmapping filters using a differentiabl
 e rendering pipeline based on a rendering loss and is able to transfer sig
 nal from normal to anisotropic roughness. As a result, we obtain a drop-in
  replacement for standard material mipmapping, offering a significant impr
 ovement in appearance preservation while still boiling down to a single pe
 r-pixel mipmap texture fetch. We report extensive experiments on two disti
 nct BRDF models.\n\nRegistration Category: FULL ACCESS, EXPERIENCE PLUS AC
 CESS, EXPERIENCE ACCESS, TRADE EXHIBITOR\n\nLanguage: ENGLISH\n\nFormat: I
 N-PERSON
URL:https://sa2022.siggraph.org/en/full-program/?id=papers_361&sess=sess15
 3
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