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DTSTAMP:20250522T212947Z
LOCATION:Mile High 1
DTSTART;TZID=America/Denver:20240731T154500
DTEND;TZID=America/Denver:20240731T173500
UID:siggraph_SIGGRAPH 2024_sess102@linklings.com
SUMMARY:Rendering, Denoising & Path Guiding
DESCRIPTION:Online Neural Path Guiding with Normalized Anisotropic Spheric
 al Gaussians\n\nWe propose a online framework to learn the spatial-varying
  distribution of the full product of the rendering equation, with a single
  small neural network using stochastic ray samples, and a novel, expressiv
 e, closed-form density model called the Normalized Anisotropic Spherical G
 aussian mixture.\n\n\nJiawei Huang (Chuzhou University, Void Dimensions); 
 Akito Iizuka and Hajime Tanaka (Tohoku University); Taku Komura (Universit
 y of Hong Kong); Yoshifumi Kitamura (Tohoku University); and Jiawei Huang\
 n---------------------\nReal-time Path Guiding Using Bounding Voxel Sampli
 ng\n\nWe propose a real-time path guiding method, Voxel Path Guiding (VXPG
 ), that significantly improves fitting efficiency under limited sampling b
 udget. We show that our method can outperform other real-time path guiding
  and virtual point light methods, particularly in handling complex dynamic
  scenes.\n\n\nHaolin Lu, Wesley Chang, Trevor Hedstrom, and Tzu-Mao Li (Un
 iversity of California San Diego)\n---------------------\nTemporally Stabl
 e Metropolis Light Transport Denoising Using Recurrent Transformer Blocks\
 n\nWe propose a learning-based denoising method for Metropolis Light Trans
 port (MLT) based on recurrent Transformer blocks. We show that our Transfo
 rmer architecture can more effectively resolve the correlation artifacts c
 ompared to the blending-based approaches used in previous work.\n\n\nChuha
 o Chen (University of California San Diego), Yuze He (Tsinghua University)
 , and Tzu-mao Li (University of California San Diego)\n-------------------
 --\nSpecular Polynomials\n\nA reformulation of specular constraints into p
 olynomial systems that enables efficiently finding a complete set of all a
 dmissible specular paths connecting two arbitrary endpoints in a scene, by
  converting the problem into finding zeros of the determinant of univariat
 e matrix polynomials.\n\n\nZhimin Fan, Jie Guo, Yiming Wang, and Tianyu Xi
 ao (Nanjing University); Hao Zhang (Southeast University); Chenxi Zhou and
  Zhenyu Chen (Nanjing University); Pengpei Hong (University of Utah); Yanw
 en Guo (Nanjing University); and Ling-Qi Yan (University of California San
 ta Barbara)\n---------------------\nSpin-weighted Spherical Harmonics for 
 Polarized Light Transport\n\nWe introduce polarized spherical harmonics (P
 SH), based on spin-weighted spherical harmonics theory, offering a rotatio
 n-invariant representation of Stokes vector fields. Our approach includes 
 frequency domain formulations of polarized rendering and spherical convolu
 tion with PSH, making it the firs...\n\n\nShinyoung Yi, Donggun Kim, and J
 iwoong Na (Korea Advanced Institute of Science and Technology (KAIST)); Xi
 n Tong (Microsoft Research Asia); and Min H. Kim (Korea Advanced Institute
  of Science and Technology (KAIST))\n---------------------\nTarget-aware I
 mage Denoising for Inverse Monte Carlo Rendering\n\nWe present a novel ima
 ge denoiser to improve the convergence of inverse rendering optimization, 
 which infers scene parameters by matching a rendering image to a user-spec
 ified target image. We reformulate a regression-based denoiser using the t
 arget image to make the optimization with our denoising ...\n\n\nJeongmin 
 Gu and Jonghee Back (Gwangju Institute of Science and Technology), Sung-Eu
 i Yoon (Korea Advanced Institute of Science and Technology (KAIST)), and B
 ochang Moon (Gwangju Institute of Science and Technology)\n---------------
 ------\nPractical Error Estimation for Denoised Monte Carlo Image Synthesi
 s\n\nWe present a practical error estimation technique for denoised Monte 
 Carlo ray tracing, using aggregated estimates of bias and variance to dete
 rmine the pixel’s squared error distribution. This leads to a novel stoppi
 ng criterion for denoised Monte Carlo image synthesis, that efficiently te
 rmi...\n\n\nArthur Firmino (Luxion, Technical University of Denmark); Ravi
  Ramamoorthi (University of California San Diego); Jeppe Revall Frisvad (T
 echnical University of Denmark); and Henrik Wann Jensen (Luxion)\n--------
 -------------\nRendering, Denoising & Path Guiding - Interactive Discussio
 n\n\nAfter the summary presentations, attendees will participate in an int
 eractive discussion. Distributed around the room will be a series of poste
 r boards for authors to gather around with the audience. Authors are invit
 ed to bring any material related to their paper that could instigate furth
 er conver...\n\n\nInterest Area: Research & Education\n\nKeyword: Renderin
 g\n\nRegistration Category: Full Conference, Full Conference Supporter, Vi
 rtual Access, Exhibitor Full Conference, Wednesday\n\nSession Chair: Gurpr
 it Singh (Max Planck Institute for Informatics)
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