As augmented reality (AR) systems proliferate and the technology gets smaller and less intrusive, we imagine a future where many AR users will interact in the same physical locations (e.g., in shared work places and public spaces). While previous research has explored AR collaboration in these spaces, our focus is on co-located but independent work. In this paper, we explore co-located AR user behavior and investigate techniques for promoting awareness of personal workspace boundaries. Specifically, we compare three techniques: showing all virtual content, visualizing bounding box outlines of content, and a self-defined workspace boundary. The findings suggest that a self-defined boundary led to significantly more personal workspace encroachments.

This article introduces Cartograph, a visualization system that harnesses the vast world knowledge encoded within Wikipedia to create thematic maps of almost any data. Cartograph extends previous systems that visualize non-spatial data using geographic approaches. Although these systems required data with an existing semantic structure, Cartograph unlocks spatial visualization for a much larger variety of datasets by enhancing input datasets with semantic information extracted from Wikipedia. Cartograph’s map embeddings use neural networks trained on Wikipedia article content and user navigation behavior. Using these embeddings, the system can reveal connections between points that are unrelated in the original datasets but are related in meaning and therefore embedded close together on the map. We describe the design of the system and key challenges we encountered. We present findings from two user studies exploring design choices and use of the system.

We present Yea Big, Yea High - a 3D user interface for surface selection in virtual environments. The interface extends previous selection interfaces that support exploratory visualization and 3D modeling. While these systems primarily focus on selecting single objects, Yea Big, Yea High allows users to select part of a surface mesh, a common task for data analysis, model editing, or annotation. The selection can be progressively refined by physically indicating a region of interest between a user's hands. We describe the design of the interface and key challenges we encountered. We present findings from a case study exploring design choices and use of the system.

This paper introduces Cartograph, a visualization system that harnesses the vast amount of world knowledge encoded within Wikipedia to create thematic maps of almost any data. Cartograph extends previous systems that visualize non-spatial data using geographic approaches. While these systems required data with an existing semantic structure, Cartograph unlocks spatial visualization for a much larger variety of datasets by enhancing input datasets with semantic information extracted from Wikipedia. Cartograph's map embeddings use neural networks trained on Wikipedia article content and user navigation behavior. Using these embeddings, the system can reveal connections between points that are unrelated in the original data sets, but are related in meaning and therefore embedded close together on the map. We describe the design of the system and key challenges we encountered, and we present findings from an exploratory user study

Three-dimensional modeling has long been regarded as an ideal application for virtual reality (VR), but current VR-based 3D modeling tools suffer from two problems that limit creativity and applicability: (1) the lack of control for freehand modeling, and (2) the difficulty of starting from scratch. To address these challenges, we present Lift-Off, an immersive 3D interface for creating complex models with a controlled, handcrafted style. Artists start outside of VR with 2D sketches, which are then imported and positioned in VR. Then, using a VR interface built on top of image processing algorithms, 2D curves within the sketches are selected interactively and "lifted" into space to create a 3D scaffolding for the model. Finally, artists sweep surfaces along these curves to create 3D models. Evaluations are presented for both long-term users and for novices who each created a 3D sailboat model from the same starting sketch. Qualitative results are positive, with the visual style of the resulting models of animals and other organic subjects as well as architectural models matching what is possible with traditional fine art media. In addition, quantitative data from logging features built into the software are used to characterize typical tool use and suggest areas for further refinement of the interface.

We present a prop-based, tangible interface for 3D interactive visualization of thin fiber structures. These data are commonly found in current bioimaging datasets, for example second-harmonic generation microscopy of collagen fibers in tissue. Our approach uses commodity visualization technologies such as a depth sensing camera and low-cost 3D display. Unlike most current uses of these emerging technologies in the games and graphics communities, we employ the depth sensing camera to create a fish-tank stereoscopic virtual reality system at the scientist's desk that supports tracking of small-scale gestures with objects already found in the work space. We apply the new interface to the problem of interactive exploratory visualization of three-dimensional thin fiber data. A critical task for the visual analysis of these data is understanding patterns in fiber orientation throughout a volume.The interface enables a new, fluid style of data exploration and fiber orientation analysis by using props to provide needed passive-haptic feedback, making 3D interactions with these fiber structures more controlled. We also contribute a low-level algorithm for extracting fiber centerlines from volumetric imaging. The system was designed and evaluated with two biophotonic experts who currently use it in their lab. As compared to typical practice within their field, the new visualization system provides a more effective way to examine and understand the 3D bioimaging datasets they collect.

In this work, we explore the potential of combining virtual reality (VR) visualizations of data with physical models generated via rapid 3D prototyping to provide a new style of exploratory data visualization. Holding a physical rapid prototype model in one's hand can provide an immediate and more accurate understanding of a complex 3D form than can be provided via a computer display, even a head-tracked VR display. However, physical printouts are static and only show the bounding surface (the outside) of a 3D geometry. When working with many of today's scientific datasets, for example, analyzing results of high-performance simulations of cardiovascular fluid dynamics [Simon et al. 2010], scientists need to understand both a complex bounding surface for the data and multivariate volumetric data contained within it. We present the initial design of a new interface for exploring this type of data. Our goal is to combine the intuitive shape understanding made possible by physical 3D rapid prototypes with complementary VR visualizations of the data inside the printed geometry.