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TZID:Asia/Seoul
X-LIC-LOCATION:Asia/Seoul
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DTSTART:18871231T000000
DTSTART:19881009T020000
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BEGIN:VEVENT
DTSTAMP:20230103T035307Z
LOCATION:Auditorium\, Level 5\, West Wing
DTSTART;TZID=Asia/Seoul:20221206T100000
DTEND;TZID=Asia/Seoul:20221206T120000
UID:siggraphasia_SIGGRAPH Asia 2022_sess153_papers_563@linklings.com
SUMMARY:Reconstructing editable prismatic CAD from rounded voxel models
DESCRIPTION:Technical Papers\n\nReconstructing editable prismatic CAD from
  rounded voxel models\n\nLambourne, Willis, Jayaraman, Zhang, Sanghi...\n\
 nReverse Engineering a CAD shape from other representations is an importan
 t geometric processing step for many downstream applications. In this work
 , we introduce a novel neural network architecture to solve this challengi
 ng task and approximate a smoothed signed distance function with an editab
 le, constrained, prismatic CAD model. During training, our method reconstr
 ucts the input geometry in the voxel space by decomposing the shape into a
  series of 2D profile images and 1D envelope functions.  These can then be
  recombined in a differentiable way allowing a geometric loss function to 
 be defined. During inference, we obtain the CAD data by first searching a 
 database of 2D constrained sketches to find curves which approximate the p
 rofile images, then extrude them and use Boolean operations to build the f
 inal CAD model. Our method approximates the target shape more closely than
  other methods and outputs highly editable constrained parametric sketches
  which are compatible with existing CAD software.\n\nRegistration Category
 : FULL ACCESS, EXPERIENCE PLUS ACCESS, EXPERIENCE ACCESS, TRADE EXHIBITOR\
 n\nLanguage: ENGLISH\n\nFormat: IN-PERSON
URL:https://sa2022.siggraph.org/en/full-program/?id=papers_563&sess=sess15
 3
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