Abstract

This article provides a snapshot of immersive virtual reality (IVR) use for scientific visualization, in the context of the evolution of computing in general and of user interfaces in particular. The main thesis of this article is that IVR has great potential for dealing with the serious problem of exponentially growing scientific datasets. Our ability to produce large datasets both through numerical simulation and through data acquisition via sensors is outrunning our ability to make sense of those datasets. While our idea of “large” datasets used to be measured in hundreds of gigabytes, based at least in part on what we could easily store, manipulate, and display in real time, today's science and engineering are producing terabytes and soon even petabytes, both from observation via sensors and as output from numerical simulation. Clearly, visualization by itself will not solve the problem of understanding truly large datasets that would overwhelm both display capacity and the human visual system. We advocate a human–computer partnership that draws on the strengths of each partner, with algorithmic culling and feature-detection used to identify the small fraction of the data that should be visually examined in detail by the human. Our hope is that IVR will be a potent tool to let humans “see” patterns, trends, and anomalies in their data well beyond what they can do with conventional 3D desktop displays.

Article Outline

1. Overview

2. Trends in computing and communication

2.1. Price/performance

2.2. Ubiquitous and mobile computing

2.2.1. Wireless

2.2.2. Profusion of form factors

2.2.3. Embedded and invisible devices

2.2.4. Federation of devices

2.3. “Netification” of middleware and applications

2.4. IVR and AR

2.5. Collaboration as major working and playing mode

3. Trends in user interfaces

3.1. Introduction

3.2. Evolution towards post-WIMP UIs

3.3. Post-WIMP UIs for IVR

3.4. Group interaction

3.5. Agent intermediation

4. IVR for scientific visualization

4.1. Introduction

4.1.1. Vision: the holodeck

4.2. Motivation for IVR

4.2.1. Context and presence

4.2.2. 3D spatial relations—body-centred judgements

4.2.3. Multidimensional data

4.3. Uses of IVR

4.3.1. Human scale

4.3.2. Non-human-scale scientific visualization

4.3.3. Teaching difficult skills

4.3.4. Tele-immersive collaboration

5. Where are we now?

5.1. Methodology issues

5.1.1. Design

5.2. Archeological data analysis

5.2.1. Research issues

5.3. Bioflow visualization

5.3.1. Research issues

5.4. Brain white-matter visualization

5.4.1. Research issues

5.5. Mars terrain Exploration

5.5.1. Research issues

6. Tele-immersion: an animating vision

6.1. UNC's office of the future

6.2. ETH-Zurich blue-c project

6.3. Electronic books for teaching surgical procedures

6.3.1. Research issues

7. Conclusion

Acknowledgements

References