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The need for additional examples (i.e., case studies) of how to organize data and perform rendering of different types of ensembles. We know perspective-tracked, stereoscopic displays can outperform desktop tools for spatial perception tasks (Ware and Mitchell, 2005, 2008), but designing effective VR visualization tools is challenging and requires synthesizing and refining user interface research results on bimanual interaction (Hinckley et al., 1997), navigation (Stoakley et al., 1995), selection (Bowman and Hodges, 1997), and manipulation (Mapes and Moshell, 1995) (citations limited here to some early, seminal works) these VR and 3D user interface research results are not always widely cited and used in scientific visualization. The lack of ensemble visualization techniques, including user interfaces, designed specifically for use in virtual reality (VR) environments. The lack of connection between the research on ensemble visualization and theoretical research on comparative visualization (Gleicher et al., 2011 Gleicher, 2017 Kim et al., 2017) which discusses fundamental trade-offs between perceptual strategies required for making comparisons, such as juxtaposition (side-by-side), superposition (overlayed), interchangeable (animating through or interactively switching between viewing a single data instance at a time), explicit encoding (e.g., computing the difference between data instances), and hybrid approaches. Unfortunately for scientists and engineers, much work remains-successful ensemble visualization requires not just a minor adjustment of the traditional visualization pipeline but rather a significant reworking. Visualization can help, and recent ensemble visualization research has made it possible to: (1) manage and render some of the large datasets that are encountered with ensembles (Vohl et al., 2016) (2) use interactive techniques to navigate through large ensemble parameter spaces (Sedlmair et al., 2014), including using both local-to-global (Coffey et al., 2013) and global-to-local approaches (Bruckner and Moller, 2010) and (3) use simulation steering to explore “what if” scenarios (Waser et al., 2010, 2014). Analyzing these ensembles is a challenging task that involves not just understanding specific data values and trends but also making comparisons. Science and engineering workflows increasingly rely upon ensembles-“concrete distributions of data, in which each outcome can be uniquely associated with a specific run or set of simulation parameters” (Obermaier and Joy, 2014).
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. The engineers confirmed hypotheses and developed new insights using a Bento Box visualization.
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 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. 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). We present Bento Box, a virtual reality data visualization technique and bimanual 3D user interface for exploratory analysis of 4D data ensembles.