2026/04/05 by Egor Fokin, Manolis Savva · 1 voice
Computer Science · #Bounding overwatch #Collision detection #Computational Geometry and Mesh Generation #Convex combination #Convex function #Convex optimization #Decomposition #Granularity #Regular polygon #Robotic Path Planning Algorithms #Stochastic Gradient Optimization Techniques #cs.CG #cs.GR
paper · pdf · open access · doi:10.48550/arxiv.2604.04244
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2026/04/05 · arxiv published 2026/04/05 · arxiv updated 2026/04/05 · openalex created_date 2026/04/08 · openalex updated_date 2026/07/28
Physics-based simulation involves trade-offs between performance and accuracy. In collision detection, one trade-off is the granularity of collider geometry. Primitive-based colliders such as bounding boxes are efficient, while using the original mesh is more accurate but often computationally expensive. Approximate Convex Decomposition (ACD) methods strive for a balance of efficiency and accuracy. Prior works can produce high-quality decompositions but require large numbers of convex parts and are sensitive to the orientation of the input mesh. We address these weaknesses with VisACD, a visibility-based, rotation-equivariant, and intersection-free ACD algorithm with GPU acceleration. Our approach produces high-quality decompositions with fewer convex parts, is not sensitive to shape orientation, and is more efficient than prior work.