Identifying the Benefits of Immersion in Virtual Reality for Volume Data Visualization

Researchers have traditionally used desktop display systems for visualizing and analyzing volume data. This is partially due to the lack of empirical results showing benefits of immersion for analysis of volume data, and also due to the cost of highly immersive virtual reality (VR) platforms. Researchers exploring the benefits of immersion tend to compare entire display systems rather than evaluating the benefits of individual components of immersion. The VR community needs controlled experimentation to gather empirical data on the benefits of individual components of immersion. In order to generalize the results to a variety of domains, a taxonomy that classifies tasks performed with volume data into general categories is also needed. In our work, we have developed a preliminary task taxonomy and are performing studies to identify the effects of various components of immersion on volume data analysis tasks. Keywords-Task taxonomy, benefits of immersion, volume visualization, controlled experiments, virtual reality, virtual environments, immersive visualization. I. VOLUME VISUALIZATION AND THE USE OF VR Visually analyzing and exploring data in volumetric format is a common task for researchers from various domains. Volume visualization is used in medicine (e.g., functional MRI data from brain scans, CT scans of the heart or lungs), in cell biology (e.g., data from confocal microscopy), in geology (e.g., rock strata), in paleontology (e.g., fossil scans), and in many other disciplines [1]. Traditionally, scientists and researchers in these various domains have used desktop computer systems for visualizing and analyzing volume data. These systems have monoscopic rendering, a small field of regard (FOR) and field of view (FOV), and a small display size, and lack head-tracked rendering. Many people have suggested using virtual reality (VR) systems with higher levels of immersion (the objective level of sensory fidelity provided by a system [2]) for scientific visualization, including the analysis of volume data, since immersive VR is designed to display spatially complex structures in a manner easier to understand and explore [3]. But there is little empirical evidence validating these claims. II. HOW MUCH IMMERSION IS ENOUGH? To validate the claims of the benefits of immersion, we can run empirical experiments comparing the effectiveness of different levels of immersion for analyzing visualizations of volume data. Researchers exploring benefits of immersion have traditionally compared specific systems in a wholesale fashion (e.g., desktop vs. CAVE vs. fishtank VR [4]). These experiments are of great value to the VR research community. They demonstrate the benefits of immersion beyond the impressive visual appeal of VR. But the results of these experiments are limited in two important ways. A. Limitation 1: Lack of generalizability of results to other VR systems When entire VR systems are compared to one another, several components of immersion (e.g., FOR, FOV, stereoscopy, head-tracked rendering) vary simultaneously between conditions. If such a study identifies a benefit of immersion, we cannot know which component(s) or combination of components of immersion resulted in those benefits. As a result of this confound, we cannot generalize the results to VR systems beyond the specific systems that were studied. We do not know whether VR systems with an intermediate level of immersion might have delivered the same benefits as a highly immersive system. The importance of these finer differentiations stems from the costliness of highly immersive VR hardware such as CAVEs or headmounted displays (HMDs). Also, given the availability of cheaper commodity VR hardware offering moderate levels of some components of immersion, we need finer-grained empirical results to determine whether such systems might have a more favorable cost-benefit ratio. B. Limitation 2: Lack of generalizability of results to other domains The second limitation arises due to the fact that experiments must evaluate the benefits of immersion using datasets and tasks from specific domains. Thus, it is difficult to apply the results to other domains and tasks. For example, a study showing that immersive VR is beneficial for analyzing volumetric brain scan data is of little import to the geologist. To realize the benefits of immersive virtual reality for volumetric data analysis for a broader audience, we need to establish the benefits of immersion in a manner generalizable across various disciplines, but it is impractical to do this by evaluating all possible datasets, tasks, and domains. We need a deeper understanding of the tasks involved in visual analysis of volume datasets if we are to make more general claims about the benefits of immersion.