Abstract
In this paper, we present a novel out-of-core technique for the interactive computation of isosurfaces from volume data. Our algorithm minimizes the main memory and disk space requirements on the visualization workstation, while speeding up isosurface extraction queries. Our overall approach is a two-level indexing scheme. First, by our meta-cell technique, we partition the original dataset into clusters of cells, called meta-cells. Secondly, we produce meta-intervals associated with the meta-cells, and build an indexing data structure on the meta-intervals. We separate the cell information, kept only in meta-cells in disk, from the indexing structure, which is also in disk and only contains pointers to meta-cells. Our meta-cell technique is an I/O-ef.cient approach for computing a k-d-tree-like partition of the dataset. Our indexing data structure, the binary-blocked I/O interval tree, is a new I/O-optimal data structure to perform stabbing queries that report from a set of meta-intervals (or intervals) those containing a query value q. Our tree is simpler to implement, and is also more space-efficient in practice than the existing structures. To perform an isosurface query, we first query the indexing structure, and then use the reported meta-cell pointers to read from disk the active meta-cells intersected by the isosurface. The isosurface itself can then be generated from active meta-cells. Rather than being a single-cost indexing approach, our technique exhibits a smooth trade-off between query time and disk space.