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TZID:Asia/Seoul
X-LIC-LOCATION:Asia/Seoul
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DTSTART:18871231T000000
DTSTART:19881009T020000
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BEGIN:VEVENT
DTSTAMP:20230103T035307Z
LOCATION:Room 324\, Level 3\, West Wing
DTSTART;TZID=Asia/Seoul:20221206T153000
DTEND;TZID=Asia/Seoul:20221206T170000
UID:siggraphasia_SIGGRAPH Asia 2022_sess155_papers_471@linklings.com
SUMMARY:Differentiable Point-Based Radiance Fields for Efficient View Synt
 hesis
DESCRIPTION:Technical Communications, Technical Papers\n\nDifferentiable P
 oint-Based Radiance Fields for Efficient View Synthesis\n\nZhang, Baek, Ru
 sinkiewicz, Heide\n\nWe propose a differentiable rendering algorithm for e
 fficient novel view synthesis. By departing from volume-based representati
 ons in favor of a learned point representation, we improve on existing met
 hods more than an order of magnitude in memory and runtime, both in traini
 ng and inference. The method begins with a uniformly-sampled random point 
 cloud and learns per-point position and view-dependent appearance, using a
  differentiable splat-based renderer to evolve the model to match a set of
  input images. Our method is up to 300x faster than NeRF in both training 
 and inference, with only a marginal sacrifice in quality, while using less
  than 10~MB of memory for a static scene. For dynamic scenes, our method t
 rains two orders of magnitude faster than STNeRF and renders at near inter
 active rate, while maintaining high image quality and temporal coherence e
 ven without imposing any temporal-coherency regularizers.\n\nRegistration 
 Category: FULL ACCESS, ON-DEMAND ACCESS\n\nLanguage: ENGLISH\n\nFormat: IN
 -PERSON, ON-DEMAND
URL:https://sa2022.siggraph.org/en/full-program/?id=papers_471&sess=sess15
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