New visualization techniques for multi-dimensional variables in complex physical domains

This work presents the new Synthesized Cell Texture (SCT) algorithm for visualizing related multiple scalar value fields within the same 3D space. The SCT method is particularly well suited to scalar quantities that could be represented in the physical domain as size fractionated particles, such as in the study of sedimentation, atmospheric aerosols, or precipitation. There are two components to this contribution. First a Scaling and Distribution (SAD) algorithm provides a means of specifying a multi-scalar field in terms of a maximum cell resolution (or density of represented values). This information is used to scale the multi-scalar field values for each 3D cell to the maximum values found throughout the data set, and then randomly distributes those values as particles varying in number, size, color, and opacity within a 2D cell slice. This approach facilitates viewing of closely spaced layers commonly found in sigma-coordinate grids. The SAD algorithm can be applied regardless of how the particles are rendered. The second contribution provides the Synthesized Cell Texture (SCT) algorithm to render the multi-scalar values. In this approach, a texture is synthesized from the location information computed by the SAD algorithm, which is then applied to each cell as a 2D slice within the volume. The SCT method trades off computation time (to synthesize the texture) and texture memory against the number of geometric primitives that must be sent through the graphics pipeline of the host system. Analysis results from a user study prove the effectiveness of the algorithm as a browsing method for multiple related scalar fields. The interactive rendering performance of the SCT method is compared with two common basic particle representations: flat-shaded color-mapped OpenGL points and quadrilaterals. Frame rate statistics show the SCT method to be up to 44 times faster, depending on the volume to be displayed and the host system. The SCT method has been successfully applied to oceanographic sedimentation data, and can be applied to other problem domains as well. Future enhancements include the extension to time-varying data and parallelization of the texture synthesis component to reduce startup time.