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DTSTAMP:20250110T023313Z
LOCATION:Hall B5 (2)\, B Block\, Level 5
DTSTART;TZID=Asia/Tokyo:20241206T131100
DTEND;TZID=Asia/Tokyo:20241206T132300
UID:siggraphasia_SIGGRAPH Asia 2024_sess146_papers_928@linklings.com
SUMMARY:NFPLight:  Deep SVBRDF Estimation via the Combination of Near and 
 Far Field Point Lighting
DESCRIPTION:Technical Papers\n\nLi Wang, Lianghao Zhang, Fangzhou Gao, Yuz
 hen Kang, and Jiawan Zhang (Tianjin University)\n\nRecovering spatial-vary
 ing bi-directional reflectance distribution function (SVBRDF) from a few h
 and-held captured images has been a challenging task in computer graphics.
  Benefiting from the learned priors from data, single-image methods can ob
 tain plausible SVBRDF estimation results. However, the extremely limited a
 ppearance information in a single image does not suffice for high-quality 
 SVBRDF reconstruction. Although increasing the number of inputs can improv
 e the reconstruction quality, it also affects the efficiency of real data 
 capture and adds significant computational burdens. Therefore, the key cha
 llenge is to minimize the required number of inputs, while keeping high-qu
 ality results. To address this, we propose maximizing the effective inform
 ation in each input through a novel co-located capture strategy that combi
 nes near-field and far-field point lighting. To further enhance effectiven
 ess, we theoretically investigate the inherent relation between two images
 . The extracted relation is strongly correlated with the slope of specular
  reflectance, substantially enhancing the precision of roughness map estim
 ation. Additionally, we designed the registration and denoising modules to
  meet the practical requirements of hand-held capture. Quantitative assess
 ments and qualitative analysis have demonstrated that our method achieves 
 superior SVBRDF estimations compared to previous approaches. All source co
 des will be publicly released.\n\nRegistration Category: Full Access, Full
  Access Supporter\n\nLanguage Format: English Language\n\nSession Chair: V
 alentin Deschaintre (Adobe Research)
URL:https://asia.siggraph.org/2024/program/?id=papers_928&sess=sess146
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