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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_578@linklings.com
SUMMARY:QuadStream: A Quad-Based Scene Streaming Architecture for Novel Vi
 ewpoint Reconstruction
DESCRIPTION:Technical Papers\n\nQuadStream: A Quad-Based Scene Streaming A
 rchitecture for Novel Viewpoint Reconstruction\n\nHladky, Stengel, Vining,
  Kerbl, Seidel...\n\nCloud rendering is attractive when targeting thin cli
 ent devices such as phones or VR/AR headsets, or any situation where a hig
 h-end GPU is not available due to thermal or power constraints. However, i
 t introduces the challenge of streaming rendered data over a network in a 
 manner that is robust to latency and potential dropouts. Current approache
 s range from streaming transmitted video and correcting it on the client--
 -which fails in the presence of disocclusion events---to solutions where t
 he server sends geometry and all rendering is performed on the client. To 
 balance the competing goals of disocclusion robustness and minimal client 
 workload, we introduce QuadStream, a new streaming technique that reduces 
 motion-to-photon latency by allowing clients to render novel views on the 
 fly and is robust against disocclusions. Our key idea is to transmit an ap
 proximate geometric scene representation to the client which is independen
 t of the source geometry and can render both the current view frame and ne
 arby adjacent views. Motivated by traditional macroblock approaches to vid
 eo codec design, we decompose the scene seen from positions in a view cell
  into a series of view-aligned quads from multiple views, or QuadProxies. 
 By operating on a rasterized G-Buffer, our approach is independent of the 
 representation used for the scene itself. Our technical contributions are 
 an efficient parallel quad generation, merging, and packing strategy for p
 roxy views that cover potential client movement in a scene; a packing and 
 encoding strategy allowing masked quads with depth information to be trans
 mitted as a frame coherent stream; and an efficient rendering approach tha
 t takes advantage of modern hardware capabilities to turn our QuadStream r
 epresentation into complete novel views on thin clients. According to our 
 experiments, our approach achieves superior quality compared both to strea
 ming methods that rely on simple video data and to geometry-based streamin
 g.\n\nRegistration Category: FULL ACCESS, EXPERIENCE PLUS ACCESS, EXPERIEN
 CE ACCESS, TRADE EXHIBITOR\n\nLanguage: ENGLISH\n\nFormat: IN-PERSON
URL:https://sa2022.siggraph.org/en/full-program/?id=papers_578&sess=sess15
 3
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