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TZID:Asia/Seoul
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DTSTART:18871231T000000
DTSTART:19881009T020000
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BEGIN:VEVENT
DTSTAMP:20230103T035306Z
LOCATION:Auditorium\, Level 5\, West Wing
DTSTART;TZID=Asia/Seoul:20221206T100000
DTEND;TZID=Asia/Seoul:20221206T120000
UID:siggraphasia_SIGGRAPH Asia 2022_sess153_papers_219@linklings.com
SUMMARY:Fast Dynamic Radiance Fields with Time-Aware Neural Voxels
DESCRIPTION:Technical Papers\n\nFast Dynamic Radiance Fields with Time-Awa
 re Neural Voxels\n\nFang, Yi, Wang, Xie, Zhang...\n\nNeural radiance field
 s (NeRF) have shown great success in modeling 3D scenes and synthesizing n
 ovel-view images. However, most previous NeRF methods take much time to op
 timize one single scene. Explicit data structures, e.g. voxel features, sh
 ow great potential to accelerate the training process. However, voxel feat
 ures face two big challenges to be applied to dynamic scenes, i.e. modelin
 g temporal information and capturing different scales of point motions. We
  propose a radiance field framework by representing scenes with time-aware
  voxel features, named as TiNeuVox. A tiny coordinate deformation network 
 is introduced to model coarse motion trajectories and temporal information
  is further enhanced in the radiance network. A multi-distance interpolati
 on method is proposed and applied on voxel features to model both small an
 d large motions. Our framework significantly accelerates the optimization 
 of dynamic radiance fields while maintaining high rendering quality. Empir
 ical evaluation is performed on both synthetic and real scenes. Our TiNeuV
 ox completes training with only 8 minutes and 8-MB storage cost while show
 ing similar or even better rendering performance than previous dynamic NeR
 F methods.\n\nRegistration Category: FULL ACCESS, EXPERIENCE PLUS ACCESS, 
 EXPERIENCE ACCESS, TRADE EXHIBITOR\n\nLanguage: ENGLISH\n\nFormat: IN-PERS
 ON
URL:https://sa2022.siggraph.org/en/full-program/?id=papers_219&sess=sess15
 3
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