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DTSTART:19721003T020000
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BEGIN:VEVENT
DTSTAMP:20260114T163654Z
LOCATION:Meeting Room C4.11\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231214T102000
DTEND;TZID=Australia/Melbourne:20231214T112500
UID:siggraphasia_SIGGRAPH Asia 2023_sess129@linklings.com
SUMMARY:From Pixels to Gradients
DESCRIPTION:Differentiable Rendering of Parametric Geometry\n\nWe propose 
 an efficient method for differentiable rendering of parametric surfaces an
 d curves, which enables their use in inverse graphics problems. Our centra
 l observation is that a representative triangle mesh can be extracted from
  a continuous parametric object in a differentiable and efficient w...\n\n
 \nMarkus Worchel and Marc Alexa (TU Berlin)\n---------------------\nDR-Occ
 luder: Generating Occluders using Differentiable Rendering\n\nThe target o
 f the occluder is to use very few faces to maintain similar occlusion prop
 erties of the original 3D model.\nIn this paper, we present DR-Occluder, a
  novel coarse-to-fine framework for occluder generation that leverages dif
 ferentiable rendering to optimize a triangle set to an occluder. Un...\n\n
 \nJiaxian Wu, Yue Lin, and Dehui Lu (NetEase Games AI Lab)\n--------------
 -------\nDiffusion Posterior Illumination for Ambiguity-aware Inverse Rend
 ering\n\nInverse rendering, the process of inferring scene properties from
  images, is a challenging inverse problem. The task is ill-posed, as many 
 different scene configurations can give rise to the same image. Most exist
 ing solutions incorporate priors into the inverse-rendering pipeline to en
 courage plaus...\n\n\nLinjie Lyu (Max-Planck-Institut für Informatik), Ayu
 sh Tewari (MIT CSAIL), Marc Habermann (Max-Planck-Institut für Informatik)
 , Shunsuke Saito and Michael Zollhöfer (Reality Labs Research), and Thomas
  Leimkühler and Christian Theobalt (Max-Planck-Institut für Informatik)\n-
 --------------------\nShaDDR: Interactive Example-Based Geometry and Textu
 re Generation via 3D Shape Detailization and Differentiable Rendering\n\nW
 e present ShaDDR, an example-based deep generative neural network which pr
 oduces a high-resolution textured 3D shape through geometry detailization 
 and conditional texture generation applied to an input coarse voxel shape.
  Trained on a small set of detailed and textured exemplar shapes, our meth
 od ...\n\n\nQimin Chen, Zhiqin Chen, Hang Zhou, and Hao Zhang (Simon Frase
 r University)\n---------------------\nTransparent Object Reconstruction vi
 a Implicit Differentiable Refraction Rendering\n\nReconstructing the geome
 try of transparent objects has been a long-standing challenge. Existing me
 thods rely on complex setups, such as manual annotation or darkroom condit
 ions, to obtain object silhouettes and usually require controlled environm
 ents with designed patterns to infer ray-background co...\n\n\nFangzhou Ga
 o, Lianghao Zhang, Li Wang, Jiamin Cheng, and Jiawan Zhang (Tianjin Univer
 sity)\n\nRegistration Category: Full Access\n\nSession Chair: Marc Stammin
 ger (Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU))
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