Institute for Data Analysis and Visualization

We present a simple and output optimal algorithm for accelerated isosurface extraction from volumetric data sets. Output optimal extraction algorithms perform an amount of work dominated by the size of the (output) isosurface rather than the size of the (input) data set. While several optimal methods have been proposed to accelerate isosurface extraction, these algorithms are relatively complicated to implement or require quantized values as input. Our method is based on a straightforward array data structure that only requires an auxiliary sorting routine for construction. The method works equally well for floating point data as it does for quantized data sets. We demonstrate how the data structure can exploit coherence between isosurfaces by performing searches incrementally. We show results for real application data validating the method's optimality.