Abstract
Virtual reality offers unique affordances that can benefit the scientific discovery process. However, virtual reality applications must maintain very high frame rates to provide immersion and prevent adverse events such as visual fatigue and motion sickness. Maintaining high frame rates can be challenging when visualizing scientific data that is large in scale. One successful technique for enabling interactive exploration of large-scale datasets is to create a large image collection from a structured sampling of camera positions, time steps, and visualization operators. This paper highlights our work to adapt this technique for virtual reality, and uses two authentic scientific datasets – a) a large-scale simulation of cancer cell transport and capture in a microfluidic device and b) a large-scale molecular dynamics simulation of graphene for creating extremely low friction interactions. We create a collection of omnidirectional stereoscopic images (three-dimensional surround-view panoramas), each of which captures all possible view angles from a given location. Therefore, virtual reality devices can always render local movements at full frame rates without loading a new image from the collection.
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Acknowledgements
This research was supported in part by the Argonne Leadership Computing Facility, which is a U.S. Department of Energy Office of Science User Facility operated under contract DE-AC02-06CH11357. This work also used the Extreme Science and Engineering Discovery Environment (XSEDE) Bridges-2 at the Pittsburgh Supercomputing Center through allocation CIS210066, which is supported by National Science Foundation grant number ACI-1548562. We would also like to acknowledge the Center for Research Computing and Data at Northern Illinois University, where computations were performed on their Gaea high-performance computing cluster. Finally, we would like to thank Michael Hood and Subramanian Sankaranarayanan for their contributions towards generating the data from the science driver simulations.
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Marrinan, T., Tan, J., Insley, J.A., Kanayinkal, A., Papka, M.E. (2022). Interactive Virtual Reality Exploration of Large-Scale Datasets Using Omnidirectional Stereo Images. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2022. Lecture Notes in Computer Science, vol 13598. Springer, Cham. https://doi.org/10.1007/978-3-031-20713-6_9
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