Adaptive Multi-Valued Volume Data Visualization Using Data-Dependent Error Metrics

Abstract

Adaptive, and especially view-dependent, volume visualization is used to display large volume data at interactive frame rates preserving high visual quality in specified or implied regions of importance. In typical approaches, the error metrics and refinement oracles used for viewdependent rendering are based on viewing parameters only. The approach presented in this paper considers viewing parameters and parameters for data exploration such as isovalues, 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 being considered in these combinations is not limited by the error metric but the ability to use them to create meaningful visualizations. Our framework supports the application of visualization methods such as isosurface extraction to adaptively refined meshes. For multi-valued data exploration purposes, we combine extracted mapping with color information and/or streamlines mapped onto an isosurface. Such a combined visualization seems advantageous, as scalar and vector field quantities can be combined visually in a highly expressive manner.