AUTHOR=Johnson Seth , Orban Daniel , Runesha Hakizumwami Birali , Meng Lingyu , Juhnke Bethany , Erdman Arthur , Samsel Francesca , Keefe Daniel F. TITLE=Bento Box: An Interactive and Zoomable Small Multiples Technique for Visualizing 4D Simulation Ensembles in Virtual Reality JOURNAL=Frontiers in Robotics and AI VOLUME=6 YEAR=2019 URL=https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2019.00061 DOI=10.3389/frobt.2019.00061 ISSN=2296-9144 ABSTRACT=

We present Bento Box, a virtual reality data visualization technique and bimanual 3D user interface for exploratory analysis of 4D data ensembles. Bento Box helps scientists and engineers make detailed comparative judgments about multiple time-varying data instances that make up a data ensemble (e.g., a group of 10 parameterized simulation runs). The approach is to present an organized set of complementary volume visualizations juxtaposed in a grid arrangement, where each column visualizes a single data instance and each row provides a new view of the volume from a different perspective and/or scale. A novel bimanual interface enables users to select a sub-volume of interest to create a new row on-the-fly, scrub through time, and quickly navigate through the resulting virtual “bento box.” The technique is evaluated through a real-world case study, supporting a team of medical device engineers and computational scientists using in-silico testing (supercomputer simulations) to redesign cardiac leads. The engineers confirmed hypotheses and developed new insights using a Bento Box visualization. An evaluation of the technical performance demonstrates that the proposed combination of data sampling strategies and clipped volume rendering is successful in displaying a juxtaposed visualization of fluid-structure-interaction simulation data (39 GB of raw data) at interactive VR frame rates.