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Bento Box: An Interactive and Zoomable Small Multiples Technique for Visualizing 4D Simulation Ensembles in Virtual Reality

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...

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Autores principales: Johnson, Seth, Orban, Daniel, Runesha, Hakizumwami Birali, Meng, Lingyu, Juhnke, Bethany, Erdman, Arthur, Samsel, Francesca, Keefe, Daniel F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805880/
https://www.ncbi.nlm.nih.gov/pubmed/33501076
http://dx.doi.org/10.3389/frobt.2019.00061
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author Johnson, Seth
Orban, Daniel
Runesha, Hakizumwami Birali
Meng, Lingyu
Juhnke, Bethany
Erdman, Arthur
Samsel, Francesca
Keefe, Daniel F.
author_facet Johnson, Seth
Orban, Daniel
Runesha, Hakizumwami Birali
Meng, Lingyu
Juhnke, Bethany
Erdman, Arthur
Samsel, Francesca
Keefe, Daniel F.
author_sort Johnson, Seth
collection PubMed
description 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.
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spelling pubmed-78058802021-01-25 Bento Box: An Interactive and Zoomable Small Multiples Technique for Visualizing 4D Simulation Ensembles in Virtual Reality Johnson, Seth Orban, Daniel Runesha, Hakizumwami Birali Meng, Lingyu Juhnke, Bethany Erdman, Arthur Samsel, Francesca Keefe, Daniel F. Front Robot AI Robotics and AI 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. Frontiers Media S.A. 2019-07-23 /pmc/articles/PMC7805880/ /pubmed/33501076 http://dx.doi.org/10.3389/frobt.2019.00061 Text en Copyright © 2019 Johnson, Orban, Runesha, Meng, Juhnke, Erdman, Samsel and Keefe. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Robotics and AI
Johnson, Seth
Orban, Daniel
Runesha, Hakizumwami Birali
Meng, Lingyu
Juhnke, Bethany
Erdman, Arthur
Samsel, Francesca
Keefe, Daniel F.
Bento Box: An Interactive and Zoomable Small Multiples Technique for Visualizing 4D Simulation Ensembles in Virtual Reality
title Bento Box: An Interactive and Zoomable Small Multiples Technique for Visualizing 4D Simulation Ensembles in Virtual Reality
title_full Bento Box: An Interactive and Zoomable Small Multiples Technique for Visualizing 4D Simulation Ensembles in Virtual Reality
title_fullStr Bento Box: An Interactive and Zoomable Small Multiples Technique for Visualizing 4D Simulation Ensembles in Virtual Reality
title_full_unstemmed Bento Box: An Interactive and Zoomable Small Multiples Technique for Visualizing 4D Simulation Ensembles in Virtual Reality
title_short Bento Box: An Interactive and Zoomable Small Multiples Technique for Visualizing 4D Simulation Ensembles in Virtual Reality
title_sort bento box: an interactive and zoomable small multiples technique for visualizing 4d simulation ensembles in virtual reality
topic Robotics and AI
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805880/
https://www.ncbi.nlm.nih.gov/pubmed/33501076
http://dx.doi.org/10.3389/frobt.2019.00061
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