Exploring Scientific Datasets using Collaborative, Immersive Visualization

CG Auditorium
Nicholas Brunhart-Lupo

We discuss the value of collaborative, immersive visualization for the exploration of scientific datasets and review techniques and tools that have been developed and are deployed at the National Renewable Energy Laboratory (NREL). We believe that collaborative visualizations that link statistical interfaces on laptops, high-performance computing (HPC), virtual reality (VR) headsets, and cave automatic virtual environments (CAVEs) enable scientific workflows that further rapid exploration of large, high-dimensional datasets by teams of analysts. Efficient workflows that blend statistical tools, general-purpose programming environments, and/or simulation with 3D visualizations that scientists and engineers manipulate in virtual reality accelerate scrutiny and discovery in datasets. We propose a categorization and loose taxonomy of these techniques, assessing their value for a variety of use cases, present a case study of the PlottyVR framework, and discuss future prospects.
In particular, the PlottyVR framework provides a two-way link between the Python or R programming languages to immersive virtual-reality visualizations, either on headsets or in walk-in spaces: an analyst at a laptop can easily push data into an immersive visualization in which another analyst can select and manipulate the data, where those selections and manipulations are fed back to the analyst at the laptop. We have achieved workflows such as online, collaborative hypothesis testing where a statistician pushes data into the VR space, a scientist constructs a hypothesis by manipulating virtual objects in that space, and then the statistician applies statistical tests to the hypothesis, all in real-time. This workflow combines the power of statistical analysis with the insightfulness of rich multidimensional data visualizations, and we have used this workflow to support both static datasets and on-demand simulations. Because using the PlottyVR tool requires only basic Python or R skills and works with commodity headsets such as the HTC VIVE, accessing such workflows has a low entry barrier and a rapid learning curve. Tools such as PlottyVR have been used at NREL to meaningfully explore multidimensional timeseries in as many as twenty dimensions by teams of as many as six persons, allowing collaborative identification of features, anomalies, and patterns in datasets that would be difficult and tedious to explore in 2D displays that limit collaborative interaction.

Speaker Description: 

Nicholas Brunhart-Lupo is a visualization scientist at the National Renewable Energy Laboratory. He received a PhD degree in computer science from the Colorado School of Mines and a master's degree in computer science from the University of Queensland, Brisbane. His research interests include 3D vector field topology, scientific visualization techniques in immersive spaces, and graphics programming technologies.

Event Category: