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BEGIN:VEVENT
DTSTAMP:20240214T070311Z
LOCATION:Meeting Room C4.8\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231213T152000
DTEND;TZID=Australia/Melbourne:20231213T162500
UID:siggraphasia_SIGGRAPH Asia 2023_sess145@linklings.com
SUMMARY:Rendering, Neural Fields & Neural Caches
DESCRIPTION:Technical Papers\n\nNeural Cache for Monte Carlo Partial Diffe
 rential Equation Solver\n\nThis paper presents a method that uses neural n
 etworks as a caching mechanism to reduce the variance of Monte Carlo Parti
 al Differential Equation solvers, such as the Walk-on-Spheres algorithm. W
 hile these Monte Carlo PDE solvers have the merits of being unbiased and d
 iscretization-free, their high ...\n\n\nZilu Li (Cornell University); Guan
 dao Yang (Cornell University, Stanford University); and Xi Deng, Christoph
 er De Sa, Bharath Hariharan, and Steve Marschner (Cornell University)\n---
 ------------------\nEfficient Visualization of Light Pollution for the Nig
 ht Sky\n\nThe artificial light sources make our daily life convenient, but
  they cause a serious problem called light pollution. \nWe propose a syste
 m for efficient visualization of the light pollution for the night sky.\nA
  number of methods have been proposed for rendering the sky, but most of t
 he methods focus...\n\n\nYoshinori Dobashi and Naoto Ishikawa (Hokkaido Un
 iversity, Prometech CG Research) and Kei Iwasaki (Saitama University, Prom
 etech CG Research)\n---------------------\nAnalysis and Synthesis of Digit
 al Dyadic Sequences\n\nWe explore the space of matrix-generated $(0, m, 2)
 $-nets and $(0, 2)$-sequences in base 2, also known as digital dyadic nets
  and sequences.\nIn computer graphics, they are arguably leading the compe
 tition for use in rendering.\nWe provide a complete characterization of th
 e design space and count the ...\n\n\nAbdalla Ahmed (King Abdullah Univers
 ity of Science and Technology (KAUST)) and Mikhail Skopenkov, Markus Hadwi
 ger, and Peter Wonka (KAUST)\n---------------------\nSeamlessNeRF: Stitchi
 ng Part NeRFs with Gradient Propagation\n\nNeural Radiance Fields (NeRFs) 
 have emerged as a promising representation for 3D scenes, sparking a surge
  in research aimed at extending the editing capabilities in this domain. T
 he task of seamless editing and merging of different NeRFs, similar to the
  "copy-and-paste" function in 2D image editing,...\n\n\nBingchen Gong and 
 Yuehao Wang (The Chinese University of Hong Kong); Xiaoguang Han (Shenzhen
  Research Institute of Big Data, the Chinese University of Hong Kong (Shen
 zhen)); and Qi Dou (The Chinese University of Hong Kong)\n----------------
 -----\nDiscontinuity-Aware 2D Neural Fields\n\nNeural image representation
 s offer the possibility of high-fidelity, compact storage, and resolution-
 independent accuracy, providing an attractive alternative to traditional p
 ixel and grid-based representations.  \nHowever, coordinate neural network
 s fail to capture discontinuities present in the ima...\n\n\nYash Belhe (U
 niversity of California San Diego); Michael Gharbi, Matt Fisher, and Iliya
 n Georgiev (Adobe Inc.); and Ravi Ramamoorthi and Tzu-Mao Li (University o
 f California San Diego)\n\nRegistration Category: Full Access\n\nSession C
 hair: Young J. Kim (Ewha Womans University)
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