Convex Primitive Decomposition for Collision Detection

2026-02-07

AI Summary

This work addresses the inefficiency and labor-intensive nature of manually creating collision geometries for 3D models by proposing a bottom-up mesh decomposition approach that automatically generates efficient, editable, and rigid-body-simulation-ready convex primitive-based collision shapes. The key innovation lies in introducing, for the first time, a quadric-surface-inspired simplification strategy into convex primitive fitting, which preserves convexity and surface coverage while enabling user-guided refinement. Evaluated on over 60 Sketchfab models using a mesh-simplification-driven fitting algorithm, Hausdorff and Chamfer distance metrics, and physical simulation benchmarks, the method consistently outperforms V-HACD and CoACD by producing more accurate collision geometries with significantly smaller volumes—occupying less than one-third the storage—and achieves notably faster simulation speeds across 24 test cases.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Computer Vision: Low Level & Physics-based Vision
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Web data generation and 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 guaranteed to enclose the input surface. We test convex primitive decomposition on over 60 models from Sketchfab, showing the algorithm's effectiveness. On this dataset, convex primitive decomposition has lower one-way mean and median Hausdorff and Chamfer distance from the collider to the input compared to V-HACD and CoACD, with less than one-third of the complexity as measured by total bytes for each collider. On top of that, rigid-body simulation performance measured by wall-clock time is consistently improved across 24 tested models.

Problem

• collision detection
• convex decomposition
• 3D modeling
• rigid body simulation
• convex primitives

Innovation

• Convex Primitive Decomposition
• Collision Detection
• Rigid Body Simulation
• Mesh Simplification
• Convex Hull Approximation