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Visualizing 3D time-varying fluid datasets is difficult because of the immense amount of data to be processed and understood. These datasets contain many evolving amorphous regions, and it is difficult to observe patterns and visually follow regions of interest. In this paper, we present a technique which isolates and tracks full-volume representations of regions of interest from 3D regular and curvilinear computational fluid dynamics datasets. Connected voxel regions ("features") are extracted from each time step and matched to features in subsequent time steps. Spatial overlap is used to determine the matching. The features from each time step are stored in octree forests to speed up the matching process. Once the features have been identified and tracked, the properties of the features and their evolutionary history can be computed. This information can be used to enhance isosurface visualization and volume rendering by color coding individual regions. We demonstrate the algorithm on four 3D time-varying simulations from ongoing research in computational fluid dynamics and show how tracking can significantly improve and facilitate the processing of massive datasets.
Silver et al. (Wed,) studied this question.