Source:Department of Computer Science, University of Calgary, Volume MSc, Calgary (2018)
Comparative visualization of multiple vector fields is frequently needed in scientific visualization to analyze static (3D) or time-varying (4D) spatial data. Efficient tools and guidelines to do this effectively are however absent. Among many available methods for comparative visualization, only superimposition or overlay techniques can display two or more data instances at the same time in the same co-registered coordinate space. As a result, while it is most effective and has many advantages, it also suffers most from occlusion and cluttering issues when working with 3D and 4D data. In this work, we present a framework for superimposed co-visualization for comparing multiple vector fields effectually from 3D and 4D data. Our framework addresses the challenges of superimposed comparative visualization in two essential ways. We propose a seeding strategy using an adaptive hierarchical grid refinement algorithm combined with importance sampling that is based on an information-theoretic probability density function that combines aspects of multiple vector fields. Furthermore, we design hybrid visual representations combining streamlines and glyphs to render the visualization in an effectively less-occluded and clutter-free way while presenting only the information necessary for comparison. Several demonstration examples are presented for validating our framework.