How do users of virtual environments perceive virtual space? Many experiments have explored this question, but most of these have used head-mounted immersive displays. This paper reports an experiment that studied large-screen immersive displays at medium-field distances of 2 to 15 meters. The experiment measured ego-centric depth judgments in a CAVE, a tiled display wall, and a real-world outdoor field as a control condition. We carefully modeled the outdoor field to make the three environments as similar as possible. Measuring egocentric depth judgments in large-screen immersive displays requires adapting new measurement protocols; the experiment used timed imagined walking, verbal estimation, and triangulated blind walking. We found that depth judgments from timed imagined walking and verbal estimation were very similar in all three environments. However, triangulated blind walking was accurate only in the out-door field; in the large-screen immersive displays it showed under-estimation effects that were likely caused by insufficient physical space to perform the technique. These results suggest using timed imagined walking as a primary protocol for assessing depth perception in large-screen immersive displays. We also found that depth judgments in the CAVE were more accurate than in the tiled display wall, which suggests that the peripheral scenery offered by the CAVE is helpful when perceiving virtual space.

Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces. It has grown out of and is strongly related to information visualization. Visual analytic tools assist analysts in detecting the expected and discovering the unexpected from complex, noisy, incomplete, heterogeneous, and sometimes deliberately deceptive data. VR/AR research has claimed for years to provide the potential for more effective environments to understand and explore information spaces. This would seem to make VR technology a natural for application to visual analytics. However, visual analytics is focused on the analytical process not the tools and technology. As such, any new methods, techniques, and technologies need to show benefit to analysts working on real problems. Additionally, the majority of visual analytics research funding is not focused on disruptive physical interface technologies. Generally speaking, new technologies and techniques for visual analytics need to function within the current analytical environment. The purpose of this panel is to introduce the domain of visual analytics to the audience and explore how and where VR/AR research can be adapted for use in visual analytics. The panelists have been selected based on topic areas of research that have potential for near term insertion or impact on visual analytics. This panel will focus on the hard issues of defining what it will take to get VR technology introduced into the visual analytics research funding stream.

Continued advances in display hardware, computing power, networking, and rendering algorithms have all converged to dramatically improve large high-resolution display capabilities. We present a survey on prior research with large high-resolution displays. In the hardware configurations section we examine systems including multi-monitor workstations, reconfigurable projector arrays, and others. Rendering and the data pipeline are addressed with an overview of current technologies. We discuss many applications for large high-resolution displays such as automotive design, scientific visualization, control centers, and others. Quantifying the effects of large high-resolution displays on human performance and other aspects is important as we look toward future advances in display technology and how it is applied in different situations. Interacting with these displays brings a different set of challenges for HCI professionals, so an overview of some of this work is provided. Finally, we present our view of the top ten greatest challenges in large highresolution displays.

In the late 90’s the emergence of high performance 3D commodity graphics cards opened the way to use PC clusters for high performance Virtual Reality (VR) applications. Today PC clusters are broadly used to drive multi projector immersive environments. In this paper, we survey the different approaches that have been developed to use PC clusters for VR applications. We review the most common software tools that enable to take advantage of the power of clusters. We also discuss some new trends.

We describe two engineering experiments designed to evaluate the effectiveness of Measurand's ShapeTape for wearable, mobile interaction. Our initial results suggest that the ShapeTape is not appropriate for interactions which require a high degree of accuracy. However, ShapeTape is capable of reproducing the qualitative motion the user is performing and thus could be used to support 3D gesture-based interaction.

We propose a new technique for gesture recognition that involves both physical and control models of gesture performance, and describe preliminary experiments done to validate the approach. The technique incorporates underlying dynamics and control models are used to augment a set of Kalman-filter-based recognizer modules so that each filters the input data under the a priori assumption that one of the gestures is being performed. The recognized gesture is the filter output that most closely matches the output of an unaugmented Kalman filter. In our preliminary experiments, we treated gestures made with simple motions of the right arm, done while tracking only hand position. We modeled the path that the hand traverses while performing a gesture as a point-mass moving through air. The control model for each specific gesture was simply an experimentally determined sequence of applied forces plus a proportional control based on spatial position. Our experiments showed that even using such a simple set of models we were able to obtain results reasonably comparable with a carefully hand-constructed feature-based discriminator on a limited set of spatially-distinct planar gestures.