Abstract

    We present a novel algorithm to perform continuous collision detection (CCD) between non-rigid, deformable models using local advancement. Given the initial and final configurations of a deformable model, our algorithm computes linear deformation by interpolating the vertices from the initial to the final configurations with a straight line path and checks for collision along that path. Our approach is applicable to polygon-soup models with arbitrary topology, handles self collisions and makes no assumption about the underlying nonrigid motion. We accelerate the algorithm by computing motion bounds on the primitives and their bounding volumes. These bounds are combined with hierarchical culling techniques and used for fast collision checking. In practice, we have observed
up to four times improvement in running time because of local advancement.

Benchmarking Scenarios

1. UNC Dynamic Scene Benchmarks

   Our method can be used to detect self-collision on dynamic scene benchmarks, and finding out the global ToC for entitle body or per-Triangle ToCs. 

Exploding Dragon (252K)                        Cloth simulation (92K)                         N-body Simulation (146K)

2. Deformable Motion Planning Benchmarks

We also used a different set of benchmarks based on motion planning in an environment composed of deformable
objects, in which both robots and obstacles can have non-rigid motions and the robots try to reach the goal configuration under such constraints such as shape preservation, collision response, manipulation and gravity forces.

Bar and Spheres (636)

RELATED LINKS

UNC Dynamic Scene Benchmarks:

    http://www.cs.unc.edu/˜geom/DynamicB/

Self-CCD: Continuous Collision Detection for Deforming Objects

    http://gamma.cs.unc.edu/SELFCD/

Open CCD: