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DTSTAMP:20240214T070248Z
LOCATION:Meeting Room C4.11\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231214T153000
DTEND;TZID=Australia/Melbourne:20231214T154500
UID:siggraphasia_SIGGRAPH Asia 2023_sess123_papers_317@linklings.com
SUMMARY:Online Scene CAD Recomposition via Autonomous Scanning
DESCRIPTION:Technical Communications, Technical Papers, TOG\n\nChanghao Li
  and Junfu Guo (University of Science and Technology of China), Ruizhen Hu
  (Shenzhen University), and Ligang Liu (University of Science and Technolo
 gy of China)\n\nAutonomous surface reconstruction of 3D scenes has been in
 tensely studied in recent years, however, it is still difficult to accurat
 ely reconstruct all the surface details of complex scenes with complicated
  object relations and severe occlusions, which makes the reconstruction re
 sults not suitable for direct use in applications such as gaming and virtu
 al reality. Therefore, instead of reconstructing the detailed surfaces, we
  aim to recompose the scene with CAD models retrieved from a given dataset
  to faithfully reflect the object geometry and arrangement in the given sc
 ene. Moreover, unlike most of the previous works on scene CAD recompositio
 n requiring an offline reconstructed scene or capture video as input, whic
 h leads to significant data redundancy, we proposed a novel online scene C
 AD recomposition method with autonomous scanning, which efficiently recomp
 oses the scene with the guidance of automatically optimized Next-Best-View
  (NBV) in a single online scanning pass. Based on the key observation that
  spatial relation in the scene can not only constrain the object pose and 
 layout optimization but also guide the NBV generation, our system consists
  of two key modules: relation-guided CAD recomposition module that uses re
 lation-constrained global optimization to get accurate object pose and lay
 out estimation, and relation-aware NBV generation module that makes the ex
 ploration during the autonomous scanning tailored for our composition task
 . Extensive experiments have been conducted to show the superiority of our
  method over previous methods in scanning efficiency and retrieval accurac
 y as well as the importance of each key component of our method.\n\nRegist
 ration Category: Full Access\n\nSession Chair: Sai-Kit Yeung (Hong Kong Un
 iversity of Science and Technology)
URL:https://asia.siggraph.org/2023/full-program?id=papers_317&sess=sess123
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