Quo Vadis CAVE – Does Immersive Visualization Still Matter?

Torsten Wolfgang Kuhlen, Bernd Hentschel
IEEE Computer Graphics and Applications Journal

More than two decades have passed since the introduction of the CAVE (Cave Automatic Virtual Environment), a landmark in the development of VR.1 The CAVE addressed two major issues with head-mounted displays of the era. First, it provided an unprecedented field of view, greatly improving the Feeling of presence in a virtual environment (VE). Second, this feeling was ampli ed because users didn’t have to rely on a virtual representation of their own bodies or parts thereof. Instead, they could physically enter the virtual space. Scientific visualization had been promulgated as a killer app for VR technology almost from day one. With the CAVE’s inception, it became possible to “put users within their data.” Proponents predicted two key advantages. First, immersive VR promised faster, more comprehensive understanding of complex, spatial relationships owing to head-tracked, stereoscopic rendering. Second, it would provide a more natural user interface, specifically for spatial interaction. In a seminal article, Andy van Dam and his colleagues proposed VR-enabled visualization as a midterm solution to the “accelerating data crisis.”2 That is, the ability to generate data had for some time outpaced the ability to analyze it. Over the years, a number of studies have investigated the effects of VR-based visualizations in speci c application scenarios. Recently, Bireswar Laha and his colleagues provided more general, empirical evidence for its benefits. Although VR and scienti c visualization have matured and many of the original technical limitations have been resolved, immersive visualization has yet to  nd the widespread, everyday use that was claimed in the early days. At the same time, the demand for scalable visualization solutions is greater than ever. If anything, the gap between data generation and analysis capabilities has widened even more. So, two questions arise. What should such scalable solutions look like, and what requirements arise regarding the underlying hardware and software and the overall methodology?

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