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Open Access Publications from the University of California

LOD-based clustering techniques for optimizing large-scale terrain storage and visualization


Large grid-digital terrain data sets used in scientific visualization, GIS and training & simulation applications are far too complex to be rendered at interactive frame rates as a whole, and easily exceed available physical main memory capacity. Therefore, to avoid excessive paging in virtual memory, the terrain data must be maintained on disk and dynamically loaded into main memory as required by the rendering algorithm. Furthermore, the elevation data must be organized in a multiresolution triangulation framework to allow efficient rendering at different levels-of-detail. In this paper, we propose novel clustering algorithms and data structures to map multiresolution terrain data to external memory such that dynamic loading (paging) of elevation data at varying level-of-detail is very efficient and minimizes the number of page faults (I/O).

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