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Abstract:

Isosurface extraction represents one of the most effective and powerful techniques for the investigation of volume datasets in Scientific Visualization. It has been used extensively in visualization and computer graphics, and plays an important role in other areas of science such as biology and medicine. In fact, all visualization packages include an isosurface extraction component. Its widespread use makes efficient isosurface extraction a very important problem. Formally, isosurface extraction is to compute and display for a given value q its isosurface C(q) = fxjF(x) = qg from a volume dataset consisting of tuples (x; F(x)), where x is a 3D sample point and F is a scalar function defined over 3D points. This process can be viewed as consisting of two phases (see Fig. 1): the search phase, in which one finds all active cells intersected by the isosurface, and the generation phase, in which one calculates the isosurface from active cells. Notice that the search phase is the bottleneck of the entire process, since in this phase one searches the 3D dataset and produces 2D data. In fact, if the dataset has N cells and there are K active cells, for typical isosurfaces the average value of K is O(N

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