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PRODID:Linklings LLC
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TZID:Asia/Seoul
X-LIC-LOCATION:Asia/Seoul
BEGIN:STANDARD
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:18871231T000000
DTSTART:19881009T020000
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BEGIN:VEVENT
DTSTAMP:20230103T035348Z
LOCATION:Room 324\, Level 3\, West Wing
DTSTART;TZID=Asia/Seoul:20221208T140000
DTEND;TZID=Asia/Seoul:20221208T153000
UID:siggraphasia_SIGGRAPH Asia 2022_sess165@linklings.com
SUMMARY:Reconstruction and Repair
DESCRIPTION:Technical Papers\n\nThe presentations will be followed by a 30
 -min Interactive Discussion Session at Room 325-CD.\n\nThe Technical Paper
 s program is the premier international forum for disseminating new scholar
 ly work in computer graphics and interactive techniques. Technical Papers 
 are published as a special issue of ACM Transactions on Graphics. In addit
 ion to papers selected by the SIGGRAPH Asia 2022 Technical Papers Jury, th
 e conference presents papers that have been published in ACM Transactions 
 on Graphics during the past year. Accepted papers adhere to the highest sc
 ientific standards.\n\nNeuralRoom: Geometry-Constrained Neural Implicit Su
 rfaces for Indoor Scene Reconstruction\n\nWang, Li, Jiang, Zhou, Cao...\n\
 nWe present a novel neural surface reconstruction method called NeuralRoom
  for reconstructing room-sized indoor scenes directly from a set of 2D ima
 ges. Recently, implicit neural representations have become a promising way
  to reconstruct surfaces from multiview images due to their high-quality r
 esult...\n\n---------------------\nStochastic Poisson Surface Reconstructi
 on\n\nSellán, Jacobson\n\nWe introduce a novel statistical derivation of t
 he classic Poisson Surface Reconstruction algorithm for recovering shapes 
 from 3D point clouds. Instead of thorugh an implicit function, we represen
 t the reconstructed shape as a modified Gaussian Process, which allows us 
 to respond to statistical quer...\n\n---------------------\nShape Completi
 on with Points in the Shadow\n\nZhang, Zhao, Wang, Hu\n\nSingle-view point
  cloud completion aims to recover the full geometry of an object based on 
 only limited observation, which is extremely hard due to the data sparsity
  and occlusion. The core challenge is to generate plausible geometries to 
 fill the unobserved part of the object based on a partial sca...\n\n------
 ---------------\nRFEPS: Reconstructing Feature-line Equipped Polygonal Sur
 face\n\nXu, Wang, Dou, Zong, Xin...\n\nFeature lines are important geometr
 ic cues in characterizing the structure of a CAD model. Despite great prog
 ress in both explicit reconstruction and implicit reconstruction, it remai
 ns a challenging task to reconstruct a polygonal surface equipped with fea
 ture lines, especially when the input point...\n\n---------------------\nA
  Neural Galerkin Solver for Accurate Surface Reconstruction\n\nHuang, Chen
 , Hu\n\nTo reconstruct meshes from the widely-available 3D point cloud dat
 a, implicit shape representation is among the primary choices as an interm
 ediate form due to its superior representation power and robustness in top
 ological optimizations. Although different parameterizations of the implic
 it fields ha...\n\n---------------------\nDeepJoin: Learning a Joint Occup
 ancy, Signed Distance, and Normal Field Function for Shape Repair\n\nLamb,
  Banerjee, Banerjee\n\nWe introduce DeepJoin, an automated approach to gen
 erate high-resolution repairs for fractured shapes using deep neural netwo
 rks. Existing approaches to perform automated shape repair operate exclusi
 vely on symmetric objects, require a complete proxy shape, or predict rest
 oration shapes using low-re...\n\n\nRegistration Category: FULL ACCESS, ON
 -DEMAND ACCESS\n\nLanguage: ENGLISH\n\nFormat: IN-PERSON, ON-DEMAND
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