Progressive Volume Models for Rectilinear Data using Tetrahedral Coons Volumes

Abstract

We present a new technique for modeling rectilinear volume data. The algorithm produces a trivariate model, F(x; y; z), which is piecewise defined over tetrahedra that fits the volume data to within a user specified tolerance. The technique is adaptive leading to an efficient model that is more complex where the data demands it. The novelty of the present technique is that a valid tetrahedrization is not required. Tetrahedral cells are subdivided as required by the error condition only. This type of cellular decomposition leads to a continuous model by the use of a tetrahedral Coons volume which has the ability to interpolate to arbitrary boundary data.