Selection classification for interaction with immersive volumetric visualizations

Abstract

Visualization enables scientists to transform data in its raw form to a visual form that will facilitate discoveries and insights. Although there are advantages for displaying inherently 3-dimensional (3D) data in immersive environments, those advantages are hampered by the challenges involved in selecting volumes of that data for exploration or analysis. Selection involves the user identifying a set of points for a specific task. This paper preliminary data collection on natural user actions for volume selection. This paper also presents a research agenda outlining an extension for volume selection classification, as well as challenges, for designing components for a direct selection of volumes of data points. © 2014 Springer International Publishing.