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DTSTAMP:20240214T070247Z
LOCATION:Meeting Room C4.8\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231214T140000
DTEND;TZID=Australia/Melbourne:20231214T141000
UID:siggraphasia_SIGGRAPH Asia 2023_sess151_papers_759@linklings.com
SUMMARY:Neural Collision Fields for Triangle Primitives
DESCRIPTION:Technical Papers\n\nRyan Zesch (Texas A&M University), Vismay 
 Modi (University of Toronto), Shinjiro Sueda (Texas A&M University), and D
 avid Levin (University of Toronto)\n\nWe present neural collision fields a
 s an alternative to contact point sampling in physics simulations.\nOur ap
 proach is built on top of a novel smoothed integral formulation for the co
 ntact surface patches between two triangle meshes. By reformulating collis
 ions as an integral, we avoid issues of sampling common to many collision-
 handling algorithms. Because the resulting integral is difficult to evalua
 te numerically, we store its solution in an integrated neural collision fi
 eld --- a 6D neural field in the space of triangle pair vertex coordinates
 . Our network generalizes well to new triangle meshes without retraining. 
 We demonstrate the effectiveness of our method by implementing it as a con
 straint in a position-based dynamics framework and show that our neural fo
 rmulation successfully handles collisions in practical simulations involvi
 ng both volumetric and thin-shell geometries.\n\nRegistration Category: Fu
 ll Access\n\nSession Chair: Tao Du (Tsinghua University, Shanghai Qi Zhi I
 nstitute)
URL:https://asia.siggraph.org/2023/full-program?id=papers_759&sess=sess151
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