Contemporary scientific research is performed by gathering and analyzing vast amounts of data. Regardless of whether this data is the result of real-world measurements or numerical simulations, extracting information from data becomes more and more difficult as the amount of data grows. Virtual-reality data exploration is a proposed technique combining real-time visualization, interaction and virtual reality environments to address the growing size and complexity of scientific data (see Figures 1 and 2).
Figure 1: Video of a user exploring a CAT scan of a patient with severe head trauma using an HTC Vive commodity head-mounted display.
Figure 2: Video of a user exploring a 3D seismic tomographic reconstruction of the Earth's crust and mantle underneath the western United States using an HTC Vive commodity head-mounted display.
A virtual-reality data exploration system offers a user the ability to create visualizations of large "live" data sets, i.e., data sets that are transmitted from a running simulation, and to interact with these visualizations to gain better insight into the data. Virtual reality environments offer the benefits of more natural interaction using six-degree-of-freedom input devices, and better understanding of three-dimensional data using stereoscopic output.
Virtual-reality data exploration places several constraints on a framework that require design of specific data structures and algorithms. The most important requirement is the maintenance of high and constant frame rates, to not break the "suspension of disbelief" necessary to interact naturally and effectively with a virtual environment. Most "classic" visualization techniques such as isosurfaces or direct volume rendering were not designed with interactivity in mind; to make those techniques applicable to data exploration they have to be changed or implemented differently. Another requirement is genericity: To be able to deal with live data, a data exploration system cannot afford to perform expensive pre-processing before offering data for exploration; this implies that the data exploration system has to work natively on the provided data, e.g., it is not sufficient to support only a single canonical grid topology and to re-sample source data to that topology.
Figure 3: Exploring a 3D flow data set (resulting from a wind tunnel simulation) by interactively placing and dragging stream ribbons visualizing direction, velocity and vorticity of the flow. The depicted data set is defined on an unstructured (tetrahedral) grid. A video of the program used to visualize seismic tomography data in a CAVE is available for download (MPEG-1 format, 29,273KB).
The main project goals were to design and implement a framework that enables interactive exploration of large three-dimensional scientific data sets defined on different grid structures. This included the following detail goals:
Implement interactive versions of classic visualization techniques
Interactive streamlines, streamribbons, etc.
Seeded particle systems
Seeded direct volume rendering
Design and implement grid abstraction layer offering topology-independent functionality necessary to perform all selected visualization techniques.
Invent interaction paradigms for three-dimensional data in virtual environments.
The current version of our framework, referred to as 3D Visualizer, supports isosurfaces, streamlines etc. and particle systems on cartesian, rectilinear, (hexahedral) curvilinear, tetrahedral and AMR grids. Our exploration program runs on a multitude of virtual reality hardware, including FakeSpace Immersive Workbench, FakeSpace ImmersaDesk, CAVE and many display wall environments using a virtual reality library developed by ourselves. We presented a paper describing our algorithms and data structures to a workshop on hierarchical methods organized by UC Davis; the paper was later included in a book containing material presented at that workshop. The paper is available for download (PDF version, 603K).
Kreylos, O., Bethel, E.W., Ligocki, T.J. and Hamann, B., Virtual-Reality Based Interactive Exploration of Multiresolution Data, in: Farin, G., Hagen, H. and Hamann, B., eds., Hierarchical Approximation and Geometrical Methods for Scientific Visualization, Springer-Verlag, Heidelberg, Germany, pp. 205-224 (2003)
Kreylos, O., Bawden, G., Bernardin, T., Billen, M.I., Cowgill, E.S., Gold, R.D., Hamann, B., Jadamec, M., Kellogg, L.H., Staadt, O.G., Sumner, D.Y., Enabling Scientific Workflows in Virtual Reality, in: Hong Wong, K., Baciu, G., and Bao, H., eds.,"Proceedings of ACM SIGGRAPH International Conference on Virtual Reality Continuum and its Applications (VRCIA 2006)," ACM Press, New York, New York, pp. 155-162 (2006)
Kellogg, L.H., Bawden, G.W., Bernardin, T., Billen, M., Cowgill, E., Hamann, B., Jadamec, M., Kreylos, O., Staadt, O., and Sumner, D., Interactive Visualization to Advance Earthquake Simulation, Pure and Applied Geophysics 165(3/4), pp. 621-633 (2008)
Billen, M.I., Kreylos, O., Hamann, B., Jadamec, M.A., Kellogg, L.H., Staadt, O., and Sumner, D.Y., A Geoscience Perspective on Immersive 3D Gridded Data Visualization, Computers and Geosciences 34(9), pp. 1056-1072 (2008)
Stevens, E.W., Sumner, D.Y., Harwood, C.L., Crutchfield, J.P., Hamann, B., Kreylos, O., Puckett, E., and Senge, P., Understanding Microbialite Morphology Using a Comprehensive Suite of 3D Analysis Tools, Astrobiology 11(6), Mary Ann Liebert, Inc., New Rochelle, NY, pp. 509-518 (2011)