Convex Primitive Decomposition for Collision Detection
Julian Knodt, Xifeng Gao

TL;DR
This paper introduces a bottom-up convex primitive decomposition method for 3D collision objects that improves collision detection efficiency and manual editability over traditional convex hull-based approaches, demonstrated on diverse models.
Contribution
The authors propose a novel convex primitive decomposition approach inspired by quadric mesh simplification, tailored for rigid body simulation and outperforming existing methods in accuracy and performance.
Findings
Lower Hausdorff and Chamfer distances compared to V-HACD and CoACD
Reduced complexity with less than one-third of collider data
Consistent performance improvements in rigid-body simulation
Abstract
Creation of collision objects for 3D models is a time-consuming task, requiring modelers to manually place primitives such as bounding boxes, capsules, spheres, and other convex primitives to approximate complex meshes. While there has been work in automatic approximate convex decompositions of meshes using convex hulls, they are not practical for applications with tight performance budgets such as games due to slower collision detection and inability to manually modify the output while maintaining convexity as compared to manually placed primitives. Rather than convex decomposition with convex hulls, we devise an approach for bottom-up decomposition of an input mesh into convex primitives specifically for rigid body simulation inspired by quadric mesh simplification. This approach fits primitives to complex, real-world meshes that provide plausible simulation performance and are…
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Taxonomy
TopicsRobotic Path Planning Algorithms · Computational Geometry and Mesh Generation · Evacuation and Crowd Dynamics
