Interval Based Data Structure Optimization

Abstract

Isosurface extraction is a widely exploited visualization technique for volumetric data on all manner of grid representation. The basic technique is often used to explore and measure many properties of data sets of ever increasing size. Therefore, data structures and algorithms that facilitate interactive exploration and fast processing of isosurfaces of large data sets is of paramount importance. While many optimal methods have been proposed to accelerate isosurface extraction, many of these algorithms have limitations with regards to storage costs and data quantization. In some cases these limitations preclude their practical application. We present a very simple clustering and volume compression technique based on observations in the span space and show that applying this technique to existing methods can reduce their storage cost. We show results for real data validating our technique.