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DTSTAMP:20260817T171531Z
LOCATION:Hall B5 (1)\, B Block\, Level 5
DTSTART;TZID=Asia/Tokyo:20241204T114300
DTEND;TZID=Asia/Tokyo:20241204T115400
UID:siggraphasia_SIGGRAPH Asia 2024_sess112_papers_349@linklings.com
SUMMARY:Architectural Co-LOD Generation
DESCRIPTION:Runze Zhang, Shanshan Pan, and Chenlei Lv (Shenzhen University
  (SZU)); Minglun Gong (University of Guelph); and Hui Huang (Shenzhen Univ
 ersity (SZU))\n\nManaging the level-of-detail (LOD) in architectural model
 s is crucial yet challenging, particularly for effective representation an
 d visualization of buildings. Traditional approaches often fail to deliver
  controllable detail alongside semantic consistency, especially when deali
 ng with noisy and inconsistent inputs. We address these limitations with C
 o-LOD, a new approach specifically designed for effective LOD management i
 n architectural modeling. Co-LOD employs shape co-analysis to standardize 
 geometric structures across multiple buildings, facilitating the progressi
 ve and consistent generation of LODs. This method allows for precise detai
 ling in both individual models and model collections, ensuring semantic in
 tegrity. Extensive experiments demonstrate that Co-LOD effectively applies
  accurate LOD across a variety of architectural inputs, consistently deliv
 ering superior detail and quality in LOD representations.\n\nRegistration 
 Category: Full Access, Full Access Supporter\n\nLanguage Format: English L
 anguage\n\nSession Chair: Michael Wimmer (TU Wien, Technische Universität 
 Wien (TU Wien))\n\n
URL:https://asia.siggraph.org/2024/program/?id=papers_349&sess=sess112
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