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.
Authors
- Sheldon Andrews (ORCID: https://orcid.org/0000-0001-9776-117X)
- Alexandre Mercier-Aubin (ORCID: https://orcid.org/0000-0002-2668-8523)
- Joël Pelletier-Guénette
Institutions
- Université de Sherbrooke (CA)
- École de Technologie Supérieure (CA)
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
Funders
- Natural Sciences and Engineering Research Council of Canada