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DTSTAMP:20260817T171533Z
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DTSTART;TZID=Asia/Tokyo:20241205T090000
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UID:siggraphasia_SIGGRAPH Asia 2024_sess126_papers_770@linklings.com
SUMMARY:A Time-Dependent Inclusion-Based Method for Continuous Collision D
 etection between Parametric Surfaces
DESCRIPTION:Xuwen Chen and Cheng Yu (School of Intelligence Science and Te
 chnology, Peking University; State Key Laboratory of General Artificial In
 telligence); Xingyu Ni (School of Computer Science, Peking University; Sta
 te Key Laboratory of General Artificial Intelligence); Mengyu Chu (School 
 of Intelligence Science and Technology, Peking University; State Key Labor
 atory of General Artificial Intelligence); Bin Wang (Beijing Institute for
  General Artificial Intelligence (BIGAI), State Key Laboratory of General 
 Artificial Intelligence); and Baoquan Chen (School of Intelligence Science
  and Technology, Peking University; State Key Laboratory of General Artifi
 cial Intelligence)\n\nContinuous collision detection (CCD) between paramet
 ric surfaces is typically formulated as a five-dimensional constrained opt
 imization problem. In the field of CAD and computer graphics, common appro
 aches to solving this problem rely on linearization or sampling strategies
 . Alternatively, inclusion-based techniques detect collisions by employing
  5D inclusion functions, which are typically designed to represent the swe
 pt volumes of parametric surfaces over a given time span, and narrowing do
 wn the earliest collision moment through subdivision in both spatial and t
 emporal dimensions. However, when high detection accuracy is required, all
  these approaches significantly increases computational consumption due to
  the high-dimensional searching space. In this work, we develop a new time
 -dependent inclusion-based CCD framework that eliminates the need for temp
 oral subdivision and can speedup conventional methods by a factor ranging 
 from 36 to 138. To achieve this, we propose a novel time-dependent inclusi
 on function that provides a continuous representation of a moving surface,
  along with a corresponding intersection detection algorithm that quickly 
 identifies the time intervals when collisions are likely to occur. We vali
 date our method across various primitive types, demonstrate its efficacy w
 ithin the simulation pipeline and show that it significantly improves CCD 
 efficiency while maintaining accuracy.\n\nRegistration Category: Full Acce
 ss, Full Access Supporter\n\nLanguage Format: English Language\n\nSession 
 Chair: Paul Kry (McGill University)\n\n
URL:https://asia.siggraph.org/2024/program/?id=papers_770&sess=sess126
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