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DTSTART:18871231T000000
DTSTART:19881009T020000
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BEGIN:VEVENT
DTSTAMP:20230103T035311Z
LOCATION:Room 324\, Level 3\, West Wing
DTSTART;TZID=Asia/Seoul:20221208T140000
DTEND;TZID=Asia/Seoul:20221208T153000
UID:siggraphasia_SIGGRAPH Asia 2022_sess165_papers_256@linklings.com
SUMMARY:Shape Completion with Points in the Shadow
DESCRIPTION:Technical Papers\n\nShape Completion with Points in the Shadow
 \n\nZhang, Zhao, Wang, Hu\n\nSingle-view point cloud completion aims to re
 cover the full geometry of an object based on only limited observation, wh
 ich is extremely hard due to the data sparsity and occlusion. The core cha
 llenge is to generate plausible geometries to fill the unobserved part of 
 the object based on a partial scan, which is under-constrained and suffers
  from a huge solution space. Inspired by the classic shadow volume techniq
 ue in computer graphics, we propose a new method to reduce the solution sp
 ace effectively. Our method considers the camera a light source that casts
  rays toward the object. Such light rays build a reasonably constrained bu
 t sufficiently expressive basis for completion.  The completion process is
  then formulated as a point displacement optimization problem. Points are 
 initialized at the partial scan and then moved to their goal locations wit
 h two types of movements for each point: directional movements along the l
 ight rays and constrained local movement for shape refinement. We design n
 eural networks to predict the ideal point movements to get the completion 
 results. We demonstrate that our method is accurate, robust, and generaliz
 able through exhaustive evaluation and comparison. Moreover, it outperform
 s state-of-the-art methods qualitatively and quantitatively on MVP dataset
 s.\n\nRegistration Category: FULL ACCESS, ON-DEMAND ACCESS\n\nLanguage: EN
 GLISH\n\nFormat: IN-PERSON, ON-DEMAND
URL:https://sa2022.siggraph.org/en/full-program/?id=papers_256&sess=sess16
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