Continuous Navigation of Nested Abstraction Levels

Abstract

We investigate the dedicated control of multiple levels of semantic and sampling-based abstraction in 3D datasets, i. e., different types of data abstractions as opposed to sampling-based abstraction which shows more or less data. This dedicated navigation in the abstraction space facilitates the mental integration of different existing visualization techniques in many application areas including our example domain of fluid simulation. We realize the continuous abstraction control by interpolating between the levels while being able to simultaneously show multiple abstractions. We employ a halo-like shading technique based on distance fields to blend between several levels while continuously navigating between focus and context abstractions.We further add a semantic lens to find focus abstractions close to a user-defined context abstraction. Our entire implementation uses 2D image-based techniques to enable real-time performance, which seamlessly integrates within a 3D visualization tool.