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We introduce a large scale benchmark for continuous collision detection (CCD)algorithms, composed of queries manually constructed to highlight challengingdegenerate cases and automatically generated using existing simulators to covercommon cases. We use the benchmark to evaluate the accuracy, correctness, andefficiency of state-of-the-art continuous collision detection algorithms, bothwith and without minimal separation. We discover that, despite the widespreaduse of CCD algorithms, existing algorithms are either: (1) correct butimpractically slow, (2) efficient but incorrect, introducing false negativeswhich will lead to interpenetration, or (3) correct but over conservative,reporting a large number of false positives which might lead to inaccuracieswhen integrated in a simulator. By combining the seminal interval root findingalgorithm introduced by Snyder in 1992 with modern predicate design techniques,we propose a simple and efficient CCD algorithm. This algorithm is competitivewith state of the art methods in terms of runtime while conservativelyreporting the time of impact and allowing explicit trade off between runtimeefficiency and number of false positives reported.

A Large Scale Benchmark and an Inclusion-Based Algorithm for Continuous Collision Detection