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DTSTAMP:20250110T023312Z
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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:Technical Papers\n\nXuwen Chen and Cheng Yu (School of Intelli
 gence Science and Technology, Peking University; State Key Laboratory of G
 eneral Artificial Intelligence); Xingyu Ni (School of Computer Science, Pe
 king University; State Key Laboratory of General Artificial Intelligence);
  Mengyu Chu (School of Intelligence Science and Technology, Peking Univers
 ity; State Key Laboratory of General Artificial Intelligence); Bin Wang (B
 eijing Institute for General Artificial Intelligence (BIGAI), State Key La
 boratory of General Artificial Intelligence); and Baoquan Chen (School of 
 Intelligence Science and Technology, Peking University; State Key Laborato
 ry of General Artificial Intelligence)\n\nContinuous collision detection (
 CCD) between parametric surfaces is typically formulated as a five-dimensi
 onal constrained optimization problem. In the field of CAD and computer gr
 aphics, common approaches to solving this problem rely on linearization or
  sampling strategies. Alternatively, inclusion-based techniques detect col
 lisions by employing 5D inclusion functions, which are typically designed 
 to represent the swept volumes of parametric surfaces over a given time sp
 an, and narrowing down the earliest collision moment through subdivision i
 n both spatial and temporal dimensions. However, when high detection accur
 acy is required, all these approaches significantly increases computationa
 l consumption due to the high-dimensional searching space. In this work, w
 e develop a new time-dependent inclusion-based CCD framework that eliminat
 es the need for temporal subdivision and can speedup conventional methods 
 by a factor ranging from 36 to 138. To achieve this, we propose a novel ti
 me-dependent inclusion function that provides a continuous representation 
 of a moving surface, along with a corresponding intersection detection alg
 orithm that quickly identifies the time intervals when collisions are like
 ly to occur. We validate our method across various primitive types, demons
 trate its efficacy within the simulation pipeline and show that it signifi
 cantly improves CCD efficiency while maintaining accuracy.\n\nRegistration
  Category: Full Access, Full Access Supporter\n\nLanguage Format: English 
 Language\n\nSession Chair: Paul Kry (McGill University)
URL:https://asia.siggraph.org/2024/program/?id=papers_770&sess=sess126
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