Local Optimization for Robust Signed Distance Field Collision

Research output: Contribution to journalJournal articlepeer-review

  • Miles Macklin
  • Erleben, Kenny
  • Matthias Müller
  • Nuttapong Chentanez
  • Stefan Jeschke
  • Zach Corse
Signed distance fields (SDFs) are a popular shape representation for collision detection. This is due to their query efficiency, and the ability to provide robust inside/outside information. Although it is straightforward to test points for interpenetration with an SDF, it is not clear how to extend this to continuous surfaces, such as triangle meshes. In this paper, we propose a per-element local optimization to find the closest points between the SDF isosurface and mesh elements. This allows us to generate accurate contact points between sharp point-face pairs, and handle smoothly varying edge-edge contact. We compare three numerical methods for solving the local optimization problem: projected gradient descent, Frank-Wolfe, and golden-section search. Finally, we demonstrate the applicability of our method to a wide range of scenarios including collision of simulated cloth, rigid bodies, and deformable solids.
Original languageEnglish
Article number8
JournalProceedings of the ACM on Computer Graphics and Interactive Techniques
Volume3
Issue number1
Number of pages17
DOIs
Publication statusPublished - 2020

ID: 240981634