A Framework for Benchmarking Interactive Collision Detection

Abstract

Collision detection is a vital component of applications spanning myriad fields, yet there exists no means for developers to analyse the suitability of their collision detection algorithms across the spectrum of scenarios that could be encountered. To rectify this, we propose a framework for benchmarking interactive collision detection, which consists of a single generic benchmark that can be adapted using a number of parameters to create a large range of practical benchmarks. This framework allows algorithm developers to test the validity of their algorithms across a wide test space and allows developers of interactive applications to recreate their application scenarios and quickly determine the most amenable algorithm. To demonstrate the utility of our framework, we adapted it to work with three collision detection algorithms supplied with the Bullet Physics SDK. Our results demonstrate that those algorithms conventionally believed to offer the best performance are not always the correct choice. This demonstrates that conventional wisdom cannot be relied on for selecting a collision detection algorithm and that our benchmarking framework fulfils a vital need in the collision detection community. The framework has been made open source, so that developers do not have to reprogram the framework to test their own algorithms, allowing for consistent results across different algorithms and reducing development time.