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DTSTAMP:20240214T070245Z
LOCATION:Meeting Room C4.11\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231213T152000
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UID:siggraphasia_SIGGRAPH Asia 2023_sess125_papers_208@linklings.com
SUMMARY:SOL-NeRF: Sunlight Modeling for Outdoor Scene Decomposition and Re
 lighting
DESCRIPTION:Technical Papers, TOG\n\nJia-Mu Sun and Tong Wu (Institute of 
 Computing Technology, Chinese Academy of Sciences; University of Chinese A
 cademy of Sciences); Yong-Liang Yang (University of Bath); Yu-Kun Lai (Car
 diff University); and Lin Gao (Institute of Computing Technology, Chinese 
 Academy of Sciences; University of Chinese Academy of Sciences)\n\nOutdoor
  scenes often involve large-scale geometry and complex unknown lighting co
 nditions, making it difficult to decompose them into geometry, reflectance
  and illumination. Recently researchers made attempts to decompose outdoor
  scenes using Neural Radiance Fields (NeRF) and learning-based lighting an
 d shadow representations. However, diverse lighting conditions and shadows
  in outdoor scenes are challenging for learning-based models. Moreover, ex
 isting methods may produce rough geometry and normal reconstruction and in
 troduce notable shading artifacts when the scene is rendered under a novel
  illumination. To solve the above problems, we propose SOL-NeRF to decompo
 se outdoor scenes with the help of a hybrid lighting representation and a 
 signed distance field geometry reconstruction. We use a single Spherical G
 aussian (SG) lobe to approximate the sun lighting, and a first-order Spher
 ical Harmonic (SH) mixture to resemble the sky lighting. This hybrid repre
 sentation is specifically designed for outdoor settings, and compactly mod
 els the outdoor lighting, ensuring robustness and efficiency. The shadow o
 f the direct sun lighting can be obtained by casting the ray against the m
 esh extracted from the signed distance field, and the remaining shadow can
  be approximated by Ambient Occlusion (AO). Additionally, sun lighting col
 or prior and a relaxed Manhattan-world assumption can be further applied t
 o boost decomposition and relighting performance. When changing the lighti
 ng condition, our method can produce consistent relighting results with co
 rrect shadow effects. Experiments conducted on our hybrid lighting scheme 
 and the entire decomposition pipeline show that our method achieves better
  reconstruction, decomposition, and relighting performance compared to pre
 vious methods both quantitatively and qualitatively.\n\nRegistration Categ
 ory: Full Access\n\nSession Chair: Michael Gharbi (Adobe, MIT)
URL:https://asia.siggraph.org/2023/full-program?id=papers_208&sess=sess125
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