Adaptive Multi-valued Volume Data Visualization using Data-dependent Error Metrics

J.T. Gray, L. Linsen, B. Hamann, and K.I. Joy (USA)


Multiresolution, error metric, view-dependent visualiza tion, isosurfaces in scalar fields, vector fields, multi-valued data


Adaptive, and especially view-dependent, volume visual ization is used to display large volume data at interac tive frame rates preserving high visual quality in specified or implied regions of importance. In typical approaches, the error metrics and refinement oracles used for view dependent rendering are based on viewing parameters only. The approach presented in this paper considers viewing pa rameters and parameters for data exploration such as iso values, velocity field magnitude, gradient magnitude, curl, or divergence. Error metrics are described for scalar fields, vector fields, and more general multi-valued combinations of scalar and vector field data. The number of data be ing considered in these combinations is not limited by the error metric but the ability to use them to create mean ingful visualizations. Our framework supports the appli cation of visualization methods such as isosurface extrac tion to adaptively refined meshes. For multi-valued data exploration purposes, we combine extracted mapping with color information and/or streamlines mapped onto an iso surface. Such a combined visualization seems advanta geous, as scalar and vector field quantities can be combined visually in a highly expressive manner.

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