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Open Access Publications from the University of California

A Hierarchical Error-Controlled Octree Data Structure for Large-Scale Visualization


We present an octree-based approach supporting multiresolution visualization of large three-dimenstional scientific data sets. Given an irregular gridded data set, we initially impose an octree data structure of relatively low resolution, i.e., consisting of a relatively small number of cells. The construction of this initial octree structure is controlled by the original data resolution and cell-specific error value. It is thus possible to use the octree to visualize either the field function value or the local error value. The octreee data structure can be refined further in areas that are specified by a user of a visualization system: A user would identify a region in space, i.e., an octree cell, where the field is of greater interest or where octree cells carry relatively large error values. This usage of our data sturcture ensures that we use the highest resolution to render only regions of interest in a large-scale scientific data set.

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