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DTSTART:18871231T000000
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DTSTAMP:20250110T023312Z
LOCATION:Hall B5 (2)\, B Block\, Level 5
DTSTART;TZID=Asia/Tokyo:20241205T090000
DTEND;TZID=Asia/Tokyo:20241205T101000
UID:siggraphasia_SIGGRAPH Asia 2024_sess125@linklings.com
SUMMARY:(Don't) Make Some Noise: Denoising
DESCRIPTION:Technical Papers\n\nEach Paper gives a 10 minute presentation.
 \n\nFiltering-Based Reconstruction for Gradient-Domain Rendering\n\nGradie
 nt-domain rendering methods reconstruct color images based on the Poisson 
 equation with gradients from correlated sampling. The relatively low varia
 nce in the gradient estimation facilitates convergence but the inevitable 
 noises make the solving process prone to unpleasant spiky artifacts.\n\nWe
 ...\n\n\nDifei Yan and Shaokun Zheng (Tsinghua University), Ling-Qi Yan (U
 niversity of California Santa Barbara), and Kun Xu (Tsinghua University)\n
 ---------------------\nA Statistical Approach to Monte Carlo Denoising\n\n
 The stochastic nature of modern Monte Carlo (MC) rendering methods inevita
 bly produces noise in rendered images for a practical number of samples pe
 r pixel. The problem of denoising these images has been widely studied, wi
 th most recent methods relying on data-driven, pretrained neural networks.
  In ...\n\n\nHiroyuki Sakai and Christian Freude (Technical University of 
 Vienna), Thomas Auzinger (Institute of Science and Technology Austria), an
 d David Hahn and Michael Wimmer (Technical University of Vienna)\n--------
 -------------\nSpatiotemporal Bilateral Gradient Filtering for Inverse Ren
 dering\n\nIn inverse rendering, gradient-based methods, which have seen gr
 eat progress in the recent years, are typically used in conjunction with t
 he Adam optimizer. While Adam usually improves convergence by temporally f
 iltering gradients over previous iterations to reduce noise, it is not tai
 lored to inver...\n\n\nWesley Chang, Xuanda Yang, Yash Belhe, Ravi Ramamoo
 rthi, and Tzu-Mao Li (University of California San Diego)\n---------------
 ------\nNeural Kernel Regression for Consistent Monte Carlo Denoising\n\nU
 nbiased Monte Carlo path tracing that is extensively used in realistic ren
 dering produces undesirable noise, especially with low samples per pixel (
 spp). Recently, several methods have coped with this problem by importing 
 unbiased noisy images and auxiliary features to neural networks to either 
 pre...\n\n\nQi Wang (State Key Laboratory of CAD&CG, Zhejiang University);
  Pengju Qiao (Institute of Software Chinese Academy of Sciences; State Key
  Laboratory of CAD&CG, Zhejiang University); Yuchi Huo (State Key Laborato
 ry of CAD&CG, Zhejiang University; Zhejiang University); Shiji Zhai (Insti
 tute of Computing Technology, Chinese Academy of Sciences); Zixuan Xie (In
 stitute of Computing Technology, Chinese Academy of Sciences; Zhejiang Lab
 ); Rengan Xie (State Key Laboratory of CAD&CG, Zhejiang University); Wei H
 ua (Zhejiang Lab); Hujun Bao (State Key Laboratory of CAD&CG, Zhejiang Uni
 versity; Zhejiang University); and Tao Liu (Shanghai Maritime University, 
 College of Transport & Communications)\n---------------------\nOnline Neur
 al Denoising with Cross-Regression for Interactive Rendering\n\nGenerating
  a rendered image sequence through Monte Carlo ray tracing is an appealing
  option when one aims to accurately simulate various lighting effects. Unf
 ortunately, interactive rendering scenarios limit the allowable sample siz
 e for such sampling-based light transport algorithms, resulting in a...\n\
 n\nHajin Choi (Gwangju Institute of Science and Technology); Seokpyo Hong 
 (Samsung Advanced Institute of Technology); Inwoo Ha (Samsung Advanced Ins
 titute of Technology, KAIST); Nahyup Kang (Samsung Advanced Institute of T
 echnology); and Bochang Moon (Gwangju Institute of Science and Technology)
 \n\nRegistration Category: Full Access, Full Access Supporter\n\nLanguage 
 Format: English Language\n\nSession Chair: Wenzel Jakob (École Polytechniq
 ue Fédérale de Lausanne)
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