Real-Time Triangle-SDF Continuous Collision Detection 49

We introduce an efficient solution to the problem of continuous collision detection (CCD) between triangle geometry and signed distance fields (SDFs). We formulate the triangle-SDF collision problem as a novel spatio-temporal local optimization that solves for the first time of impact between a triangle and an SDF isosurface. Our method offers improved robustness over point sampling methods, and outperforms recent triangle-SDF discrete collision detection (DCD) algorithms. Furthermore, a novel method for adaptively refining the potential collision points on large triangles is proposed for robust triangle-SDF collision detection with coarse meshes. This enables the use of reduced geometry for efficient simulations. We demonstrate the benefits of our approach by comparing to state-of-the-art algorithms for triangle-SDF collision detection, and showcase its effectiveness through simulations involving complex collision scenarios.

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Publication Details

Journal
Proceedings of the ACM on Computer Graphics and Interactive Techniques
Published
2025-08-08
DOI
https://doi.org/10.1145/3747862
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
0.00

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article

Real-Time Triangle-SDF Continuous Collision Detection 49

Sheldon Andrews, Alexandre Mercier-Aubin, Joël Pelletier-Guénette
Proceedings of the ACM on Computer Graphics and Interactive Techniques
Robotic Path Planning Algorithms
article

Real-Time Triangle-SDF Continuous Collision Detection 49

Sheldon Andrews, Alexandre Mercier-Aubin, Joël Pelletier-Guénette
article en

Abstract

We introduce an efficient solution to the problem of continuous collision detection (CCD) between triangle geometry and signed distance fields (SDFs). We formulate the triangle-SDF collision problem as a novel spatio-temporal local optimization that solves for the first time of impact between a triangle and an SDF isosurface. Our method offers improved robustness over point sampling methods, and outperforms recent triangle-SDF discrete collision detection (DCD) algorithms. Furthermore, a novel method for adaptively refining the potential collision points on large triangles is proposed for robust triangle-SDF collision detection with coarse meshes. This enables the use of reduced geometry for efficient simulations. We demonstrate the benefits of our approach by comparing to state-of-the-art algorithms for triangle-SDF collision detection, and showcase its effectiveness through simulations involving complex collision scenarios.

Proceedings of the ACM on Computer Graphics and Interactive TechniquesVol. 8(4)
Université de Sherbrooke (CA), École de Technologie Supérieure (CA)
Natural Sciences and Engineering Research Council of Canada
Openalex Percentile: Top 80%
Robotic Path Planning Algorithms
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