A Multi-Scale Singularity Bounding Volume Hierarchy

Erleben, Kenny and Somchaipeng, Kerawit and Sporring, Jon

A scale space approach is taken for building bounding volume
hierarchies for collision detection. An elliptical bounding volume
is generated at each node of the bounding volume hierarchy using
estimates of the mass distribution.

Traditional top-down methods approximates the surface of an object
in coarse to fine manner, by recursively increasing resolution by
some factor, e.g. 2. The method presented in this article analyzes
the mass distribution of a solid object using a well founded
scale-space based on the Diffusion Equation: the Gaussian
Scale-Space. In the Gaussian Scale-Space, the deep structure of
extremal mass points is naturally binary, and the linking process is
therefore very simple.

The main contribution of this paper is a novel approach for
constructing bounding volume hierarchies using multi-scale
singularity-trees for collision detection. The bounding volume
hierarchy building algorithm extends the field with a new method
based on volumetric shape rather than statistics of the surface
geometry or geometrical constructs such as medial surfaces.