Title
Local Optimization for Robust Signed Distance Field Collision
Abstract
AbstractSigned 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.
Year
DOI
Venue
2020
10.1145/3384538
Proceedings of the ACM on Computer Graphics and Interactive Techniques
Keywords
DocType
Volume
Collision detection,Signed distance function,Gradient descent,Isosurface,Local search (optimization),Collision,Polygon mesh,Numerical analysis,Algorithm,Computer science
Journal
3
Issue
Citations 
PageRank 
1
1
0.40
References 
Authors
0
6
Name
Order
Citations
PageRank
Miles Macklin124817.11
Kenny Erleben223024.55
Matthias Muller32726122.09
Nuttapong Chentanez467538.02
Stefan Jeschke5164.75
Zach Corse610.40