Time of Impact Dataset for Continuous Collision Detection and a Scalable Conservative Algorithm

TL;DR

This paper presents a large-scale benchmark dataset for continuous collision detection, evaluates existing algorithms, and introduces a scalable, correct new algorithm optimized for parallel hardware, improving accuracy and efficiency.

Contribution

The paper introduces a scalable, provably correct CCD algorithm for parallel architectures and provides a comprehensive benchmark dataset with implementations for evaluation.

Findings

Several existing CCD methods have efficiency or accuracy issues.

The new algorithm is scalable and maintains correctness on modern hardware.

Benchmark dataset and implementations are publicly released for future research.

Abstract

We introduce a large-scale benchmark for broad- and narrow-phase continuous collision detection (CCD) over linearized trajectories with exact time of impacts and use it to evaluate the accuracy, correctness, and efficiency of 13 state-of-the-art CCD algorithms. Our analysis shows that several methods exhibit problems either in efficiency or accuracy. To overcome these limitations, we introduce an algorithm for CCD designed to be scalable on modern parallel architectures and provably correct when implemented using floating point arithmetic. We integrate our algorithm within the Incremental Potential Contact solver [24] and evaluate its impact on various simulation scenarios. Our approach includes a broad-phase CCD to quickly filter out primitives having disjoint bounding boxes and a narrow-phase CCD that establishes whether the remaining primitive pairs indeed collide. Our broad-phase algorithm is efficient and scalable thanks to the experimental observation that sweeping along a coordinate axis performs surprisingly well on modern parallel architectures. For narrow-phase CCD, we re-design the recently proposed interval-based algorithm of Wang et al. [45] to work on massively parallel hardware. To foster the adoption and development of future linear CCD algorithms, and to evaluate their correctness, scalability, and overall performance, we release the dataset with analytic ground truth, the implementation of all the algorithms tested, and our testing framework.