Rethinking Collision Detection on GPU Ray Tracing Architecture

2026-04-25

AI Summary

Traditional GPU ray tracing architectures suffer significant performance degradation when handling spherical particles with non-uniform radii, primarily due to overly conservative bounding volumes that induce redundant intersection tests. This work proposes Mochi, a novel approach that decouples the bounding volume hierarchy (BVH) from collision search radii by introducing object-level proxy spheres. Leveraging the symmetry of collision relationships, Mochi reformulates the proximity detection problem to generate tighter bounding volumes, ensuring completeness without missing any true collisions. The method accommodates both uniform and non-uniform particle radii while maintaining correctness. By exploiting GPU-accelerated ray tracing hardware for efficient BVH construction and traversal, Mochi achieves substantial performance gains over existing BVH- and ray tracing–based methods in large-scale particle simulations, delivering robust and scalable acceleration.

Technology Category

• Computer Vision: Motion & Tracking
• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Mixture of Experts (MoE)

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Discrete Collision Detection (DCD) is a fundamental task in several domains including particle-based physics simulations. Efficient DCD uses indexing structures such as Bounding Volume Hierarchy (BVH), but accelerating irregular BVH traversals demands meticulous efforts to achieve performance. Modern GPUs feature Ray Tracing (RT) architecture that provides hardware acceleration for BVH traversal and optimized drivers for BVH construction. Recent work has attempted to exploit RT architecture to accelerate DCD on spherical particles by reducing DCD to fixed-radius neighbor search. However, this reduction breaks down for particles with different radii, necessitating the use of large bounding boxes that result in a higher number of duplicate collisions and poor performance. To address these limitations, we present Mochi, a new reduction that reformulates DCD on RT architecture by exploiting the symmetry of collision relations to support both uniform and non-uniform spherical particles efficiently. Mochi introduces per-object proxy spheres that decouple BVH bounding volumes from the collision search radius, enabling significantly tighter bounding boxes without sacrificing correctness. Mochi is provably sound and guarantees that all true collisions are detected. We integrate Mochi into an end-to-end particle simulation pipeline and evaluate it across large-scale particle workloads, showing consistent speedups over state-of-the-art BVH-based and RT-based DCD implementations. Mochi generalizes prior RT-based neighbor search formulations while avoiding their fundamental limitations for non-uniform spheres.

Problem

• Collision Detection
• GPU Ray Tracing
• Bounding Volume Hierarchy
• Non-uniform Spheres
• Discrete Collision Detection

Innovation

• GPU Ray Tracing
• Collision Detection
• Bounding Volume Hierarchy
• Proxy Spheres
• Non-uniform Particles