Constructing isosurfaces in a localized fashion using an underlying octree data structure

We present an octree-based approach for isosurface extraction from large volumetric scalar-valued data. Given scattered points with associated function values, we impose an octree structure of relatively low resolution. Octree construction is controlled by original data resolution and cell-specific error values. For each cell in the octree, we compute an average function value and additional statistical data for the original points inside the cell. Once a specific isovalue is specified, we adjust the initial octree by expanding its leaves based on a comparison of statistics with the isovalue. We tetrahedrize the centers of the octree's cells to determine tetrahedral meshes decomposing the entire spatial domain of the data, including a possibly specific region of interest (ROI). Extracted isosurfaces are crack-free inside an ROI, but cracks can appear at the boundary of an ROI. The inital isosurface is an approximation of the exact one, but its quality suffices for a viewer to identify an ROI where more accuracy is desirable. In the refinement process, we refine affected octree nodes and update the triangulation locally to produce better isosurface representations. This adaptive and user-driven refinement provides a means for interactive data exploration via real-time and local isosurface extraction