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DTSTART:18871231T000000
DTSTART:19881009T020000
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BEGIN:VEVENT
DTSTAMP:20230103T035312Z
LOCATION:Room 324\, Level 3\, West Wing
DTSTART;TZID=Asia/Seoul:20221208T170000
DTEND;TZID=Asia/Seoul:20221208T183000
UID:siggraphasia_SIGGRAPH Asia 2022_sess167_papers_361@linklings.com
SUMMARY:MIPNet: Neural Normal-to-Anisotropic-Roughness MIP mapping
DESCRIPTION:Technical Communications, Technical Papers\n\nMIPNet: Neural N
 ormal-to-Anisotropic-Roughness MIP mapping\n\nGauthier, Faury, Levallois, 
 Thonat, Thiery...\n\nWe present MIPNet, a novel approach for SVBRDF mipmap
 ping which preserves material appearance under varying view distances and 
 lighting conditions. As in classical mipmapping, our method explicitly enc
 odes the multiscale appearance of materials in a SVBRDF mipmap pyramid. To
  do so, we use a tensor-based representation, coping with gradient-based o
 ptimization, for encoding anisotropy which is compatible with existing rea
 l-time rendering engines. Instead of relying on a simple texture patch ave
 rage for each channel independently, we propose a cascaded architecture of
  multilayer perceptrons to approximate the material appearance using only 
 the fixed material channels. Our neural model learns simple mipmapping fil
 ters using a differentiable rendering pipeline based on a rendering loss a
 nd is able to transfer signal from normal to anisotropic roughness. As a r
 esult, we obtain a drop-in replacement for standard material mipmapping, o
 ffering a significant improvement in appearance preservation while still b
 oiling down to a single per-pixel mipmap texture fetch. We report extensiv
 e experiments on two distinct BRDF models.\n\nRegistration Category: FULL 
 ACCESS, ON-DEMAND ACCESS\n\nLanguage: ENGLISH\n\nFormat: IN-PERSON, ON-DEM
 AND
URL:https://sa2022.siggraph.org/en/full-program/?id=papers_361&sess=sess16
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