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PRODID:Linklings LLC
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TZID:Asia/Tokyo
X-LIC-LOCATION:Asia/Tokyo
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TZOFFSETFROM:+0900
TZOFFSETTO:+0900
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DTSTART:18871231T000000
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BEGIN:VEVENT
DTSTAMP:20260817T171535Z
LOCATION:Hall B5 (1)\, B Block\, Level 5
DTSTART;TZID=Asia/Tokyo:20241203T144500
DTEND;TZID=Asia/Tokyo:20241203T155500
UID:siggraphasia_SIGGRAPH Asia 2024_sess106@linklings.com
SUMMARY:Modeling and PDEs
DESCRIPTION:Each Paper gives a 10 minute presentation.\n\nDifferential Wal
 k on Spheres\n\nWe introduce a Monte Carlo method for computing derivative
 s of the solution to a partial differential equation (PDE) with respect to
  problem parameters (such as domain geometry or boundary conditions). Deri
 vatives can be evaluated at arbitrary points, without performing a global 
 solve or constructin...\n\n\nBailey Miller (Carnegie Mellon Uniersity), Ro
 han Sawhney (NVIDIA), and Keenan Crane and Ioannis Gkioulekas (Carnegie Me
 llon Uniersity)\n---------------------\nProjected Walk on Spheres: A Monte
  Carlo Closest Point Method for Surface PDEs\n\nWe present projected walk 
 on spheres (PWoS), a novel pointwise and discretization-free Monte Carlo s
 olver for surface PDEs with Dirichlet boundaries, as a generalization of t
 he walk on spheres method (WoS)  [Muller 1956; Sawhney and Crane 2020]. We
  adapt the recursive relationship of WoS designed fo...\n\n\nRyusuke Sugim
 oto, Nathan King, Toshiya Hachisuka, and Christopher Batty (University of 
 Waterloo)\n---------------------\nSolving Inverse PDE Problems using Grid-
 Free Monte Carlo Estimators\n\nPartial differential equations can model di
 verse physical phenomena including heat diffusion, incompressible flows, a
 nd electrostatic potentials. Given a description of an object's boundary a
 nd interior, traditional methods solve such PDEs by densely meshing the in
 terior and then solving a large and...\n\n\nEkrem Fatih Yilmazer (EPFL), D
 elio Vicini (Google Inc.), and Wenzel Jakob (EPFL)\n---------------------\
 nDiffCSG: Differentiable CSG via Rasterization\n\nDifferentiable rendering
  is a key ingredient for inverse rendering and machine learning, as it all
 ows to optimize scene parameters (shape, materials, lighting) to best fit 
 target images. Differentiable rendering requires that each scene parameter
  relates to pixel values through differentiable operat...\n\n\nHaocheng Yu
 an (University of Edinburgh); Adrien Bousseau (Inria Sophia-Antipolis, Uni
 versité Côte d’Azur); Hao Pan (Microsoft Research Asia); Quancheng Zhang (
 Nanjing University); Niloy J. Mitra (University College London (UCL), Adob
 e Research); and Changjian Li (University of Edinburgh)\n-----------------
 ----\nText-guided Controllable Mesh Refinement for Interactive 3D Modeling
 \n\nWe propose a novel technique for adding geometric details to an input 
 coarse 3D mesh guided by a text prompt. Our method is composed of three st
 ages. First, we generate a single-view RGB image conditioned on the input 
 coarse geometry and the input text prompt. This single-view image generati
 on step ...\n\n\nYun-Chun Chen and Selena Ling (University of Toronto); Zh
 iqin Chen, Vladimir G. Kim, and Matheus Gadelha (Adobe Research); and Alec
  Jacobson (University of Toronto)\n---------------------\nI❤️MESH: A DSL f
 or Mesh Processing\n\nMesh processing algorithms are often communicated vi
 a concise mathematical notation (e.g., summation over mesh neighborhoods).
  However, conversion of notation into working code remains a time-consumin
 g and error-prone process, which requires arcane knowledge of low-level da
 ta structures and librarie...\n\n\nYong Li (South China University of Tech
 nology, George Mason University); Shoaib Kamil (Adobe Research); Keenan Cr
 ane (Carnegie Mellon University); Alec Jacobson (University of Toronto, Ad
 obe Research); and Yotam Gingold (George Mason University)\n\nRegistration
  Category: Full Access, Full Access Supporter\n\nLanguage Format: English 
 Language\n\nSession Chair: Yonghao Yue (Aoyama Gakuin University)
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