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DTSTAMP:20250110T023312Z
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:Technical Papers\n\nRunze Zhang, Shanshan Pan, and Chenlei Lv 
 (Shenzhen University (SZU)); Minglun Gong (University of Guelph); and Hui 
 Huang (Shenzhen University (SZU))\n\nManaging the level-of-detail (LOD) in
  architectural models is crucial yet challenging, particularly for effecti
 ve representation and visualization of buildings. Traditional approaches o
 ften fail to deliver controllable detail alongside semantic consistency, e
 specially when dealing with noisy and inconsistent inputs. We address thes
 e limitations with Co-LOD, a new approach specifically designed for effect
 ive LOD management in architectural modeling. Co-LOD employs shape co-anal
 ysis to standardize geometric structures across multiple buildings, facili
 tating the progressive and consistent generation of LODs. This method allo
 ws for precise detailing in both individual models and model collections, 
 ensuring semantic integrity. Extensive experiments demonstrate that Co-LOD
  effectively applies accurate LOD across a variety of architectural inputs
 , consistently delivering superior detail and quality in LOD representatio
 ns.\n\nRegistration Category: Full Access, Full Access Supporter\n\nLangua
 ge Format: English Language\n\nSession Chair: Michael Wimmer (TU Wien)
URL:https://asia.siggraph.org/2024/program/?id=papers_349&sess=sess112
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