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TZID:Asia/Tokyo
X-LIC-LOCATION:Asia/Tokyo
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TZOFFSETFROM:+0900
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BEGIN:VEVENT
DTSTAMP:20260817T171535Z
LOCATION:Hall B5 (2)\, B Block\, Level 5
DTSTART;TZID=Asia/Tokyo:20241205T092800
DTEND;TZID=Asia/Tokyo:20241205T094200
UID:siggraphasia_SIGGRAPH Asia 2024_sess125_papers_206@linklings.com
SUMMARY:Filtering-Based Reconstruction for Gradient-Domain Rendering
DESCRIPTION:Difei Yan and Shaokun Zheng (Tsinghua University), Ling-Qi Yan
  (University of California Santa Barbara), and Kun Xu (Tsinghua University
 )\n\nGradient-domain rendering methods reconstruct color images based on t
 he Poisson equation with gradients from correlated sampling. The relativel
 y low variance in the gradient estimation facilitates convergence but the 
 inevitable noises make the solving process prone to unpleasant spiky artif
 acts.\n\nWe present a gradient-guided filtering approach for reconstructio
 n, which avoids the instability from the direct usage of noisy gradients. 
 Instead, we model the output color of each pixel as a weighted combination
  of neighboring pixels, where the gradients are used as guidance to comput
 e optimized filtering weights. The gradients are enhanced before being use
 d in gradient-guided filtering. A coarse-to-fine strategy is also employed
  to make use of information from a larger scale. \n\nExperiments demonstra
 te that our method achieves the best reconstruction results for gradient-d
 omain renderings compared to existing techniques. Besides, our method has 
 two desirable properties: first, our method is not learning-based so it do
 es not require an extra training step and would be more robust for unseen 
 scenes; second, our method is designed to be asymptotic unbiased.\n\nRegis
 tration Category: Full Access, Full Access Supporter\n\nLanguage Format: E
 nglish Language\n\nSession Chair: Wenzel Jakob (École Polytechnique Fédera
 le de Lausanne (EPFL), NVIDIA)\n\n
URL:https://asia.siggraph.org/2024/program/?id=papers_206&sess=sess125
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