Robotics: Science and Systems XXII

TE-SDF: Tetra-Encoded Signed Distance Field for Memory-Efficient and Accurate Collision Detection

Harim Ji, Yongseok Lee, Dongjun Lee

Abstract:

A signed distance field (SDF) is a widely used geometric representation for robust collision detection between complex geometries, which is crucial for contact-rich simulations. While numerous works have studied SDF representations and SDF-based collision detection, achieving both memory efficiency and high accuracy while maintaining scalability remains a challenge. In this paper, we propose a novel SDF representation, Tetra-Encoded SDF (TE-SDF), which combines the adaptive spatial discretization of a tetrahedral mesh with exact-distance evaluation localized to each tetrahedron by encoding a compact set of candidate surface faces per tetrahedron. We demonstrate the effectiveness of TE-SDF in contact-rich simulation by implementing a fully GPU-accelerated collision detector based on TE-SDF and integrating it into a GPU-accelerated simulation framework. Our results show that TE-SDF enables memory-efficient, accurate, and scalable collision detection, expanding the domain of robotic simulation scenarios that can be handled in practice.

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Bibtex:

  
@INPROCEEDINGS{JiH-RSS-26, 
    AUTHOR    = {Harim Ji AND Yongseok Lee AND Dongjun Lee}, 
    TITLE     = {{TE-SDF: Tetra-Encoded Signed Distance Field for Memory-Efficient and Accurate Collision Detection}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2026}, 
    ADDRESS   = {Sydney, Australia}, 
    MONTH     = {July}, 
    DOI       = {10.15607/RSS.2026.XXII.171} 
}