Empirical, hypothesis-driven, experimentation is at the heart of the scientific discovery process and has become commonplace in human-factors related fields. To enable the integration of visual analytics in such experiments, we introduce VEEVVIE, the Visual Explorer for Empirical Visualization, VR and Interaction Experiments. VEEVVIE is comprised of a back-end ontology which can model several experimental designs encountered in these fields. This formalization allows VEEVVIE to capture experimental data in a query-able form and makes it accessible through a front-end interface. This front-end offers several multi-dimensional visualization widgets with built-in filtering and highlighting functionality. VEEVVIE is also expandable to support custom experimental measurements and data types through a plug-in visualization widget architecture. We demonstrate VEEVVIE through several case studies of visual analysis, performed on the design and data collected during an experiment on the scalability of high-resolution, immersive, tiled-display walls.

We present the results of a variable information space experiment, targeted at exploring the scalability limits of immersive highresolution, tiled-display walls under physical navigation. Our work is motivated by a lack of evidence supporting the extension of previously established benefits on substantially large, room-shaped displays. Using the Reality Deck, a gigapixel resolution immersive display, as its apparatus, our study spans four display form-factors, starting at 100 megapixels arranged planarly and up to one gi-gapixel in a horizontally immersive setting. We focus on four core tasks: visual search, attribute search, comparisons and pattern finding. We present a quantitative analysis of per-task user performance across the various display conditions. Our results demonstrate improvements in user performance as the display form-factor changes to 600 megapixels. At the 600 megapixel to 1 gigapixel transition, we observe no tangible performance improvements and the visual search task regressed substantially. Additionally, our analysis of subjective mental effort questionnaire responses indicates that subjective user effort grows as the display size increases, validating previous studies on smaller displays. Our analysis of the participants' physical navigation during the study sessions shows an increase in user movement as the display grew. Finally, by visualizing the participants' movement within the display apparatus space, we discover two main approaches (termed “overview” and “detail”) through which users chose to tackle the various data exploration tasks. The results of our study can inform the design of immersive high-resolution display systems and provide insight into how users navigate within these room-sized visualization spaces.

We introduce the concept of the infinite canvas as a metaphor for the immersive visual exploration of very large image datasets using a natural walking interface. The interface allows the user to move along the display surface and to be continuously exposed to new data, essentially exploring the horizontal axis of an arbitrarily long canvas. Our system provides a spiral navigation interface that shows a compressed immersive overview of the data and facilitates the rapid and fluid transition to distant points within the infinite canvas. We demonstrate the implementation of the infinite canvas in the world's first 1.5 billion pixel tiled immersive display.

We present a framework for acuity-driven visualization of super-high resolution image data on gigapixel displays. Tiled display walls offer a large workspace that can be navigated physically by the user. Based on head tracking information, the physical characteristics of the tiled display and the formulation of visual acuity, we guide an out-of-core gigapixel rendering scheme by delivering high levels of detail only in places where it is perceivable to the user. We apply this principle to gigapixel image rendering through adaptive level of detail selection. Additionally, we have developed an acuity-driven tessellation scheme for high-quality Focus-and-Context (F+C) lenses that significantly reduces visual artifacts while accurately capturing the underlying lens function. We demonstrate this framework on the Reality Deck, an immersive gigapixel display. We present the results of a user study designed to quantify the impact of our acuity-driven rendering optimizations in the visual exploration process. We discovered no evidence suggesting a difference in search task performance between our framework and naive rendering of gigapixel resolution data, while realizing significant benefits in terms of data transfer overhead. Additionally, we show that our acuity-driven tessellation scheme offers substantially increased frame rates when compared to naive pre-tessellation, while providing indistinguishable image quality.

We introduce NuNav3D, a body-driven 3D navigation interface for large displays and immersive scenarios. While 3D navigation is a core component of VR applications, certain situations, like remote displays in public or large visualization environments, do not allow for using a navigation controller or prop. NuNav3D maps hand motions, obtained from a pose recognition framework which is driven by a depth sensor, to a virtual camera manipulator, allowing for direct control of 4 DOFs of navigation. We present the NuNav3D navigation scheme and our preliminary user study results under two scenarios, a path-following case with tight geometrical constraints and an open space exploration case, while comparing our method against a traditional joypad controller.

In Virtual Reality, immersive systems such as the CAVE provide an important tool for the collaborative exploration of large 3D data. Unlike head-mounted displays, these systems are often only partially immersive due to space, access, or cost constraints. The resulting loss of visual information becomes a major obstacle for critical tasks that need to utilize the users' entire field of vision. We have developed a conformal visualization technique that establishes a conformal mapping between the full 360° field of view and the display geometry of a given visualization system. The mapping is provably angle-preserving and has the desirable property of preserving shapes locally, which is important for identifying shape-based features in the visual data. We apply the conformal visualization to both forward and backward rendering pipelines in a variety of retargeting scenarios, including CAVEs and angled arrangements of flat panel displays. In contrast to image-based retargeting approaches, our technique constructs accurate stereoscopic images that are free of resampling artifacts. Our user study shows that on the visual polyp detection task in Immersive Virtual Colonoscopy, conformal visualization leads to improved sensitivity at comparable examination times against the traditional rendering approach. We also develop a novel user interface based on the interactive recreation of the conformal mapping and the real-time regeneration of the view direction correspondence.

Current immersive VR systems such as the CAVE provide an effective platform for the immersive exploration of large 3D data. A major limitation is that in most cases at least one display surface is missing due to space, access or cost constraints. This partially-immersive visualization results in a substantial loss of visual information that may be acceptable for some applications, however it becomes a major obstacle for critical tasks, such as the analysis of medical data. We propose a conformal deformation rendering pipeline for the visualization of datasets on partially-immersive platforms. The angle-preserving conformal mapping approach is used to map the 360°3D view volume to arbitrary display configurations. It has the desirable property of preserving shapes under distortion, which is important for identifying features, especially in medical data. The conformal mapping is used for rasterization, realtime raytracing and volume rendering of the datasets. Since the technique is applied during the rendering, we can construct stereoscopic images from the data, which is usually not true for image-based distortion approaches. We demonstrate the stereo conformal mapping rendering pipeline in the partially-immersive 5-wall Immersive Cabin (IC) for virtual colonoscopy and architectural review.

Real-time rendering can benefit from global illumination methods to make the 3D environments look more convincing and lifelike. On the other hand, the conventional global illumination algorithms for the estimation of the diffuse surface interreflection make heavy usage of intra- and interobject visibility calculations, so they are time-consuming, and using them in real-time graphics applications can be prohibitive for complex scenes. Modern illumination approximations, such as ambient occlusion variants, use precalculated or frame-dependent data to reduce the problem to a local shading one. This paper presents a fast real-time method for visibility sampling using volumetric data in order to produce accurate inter- and intraobject ambient occlusion. The proposed volume sampling technique disassociates surface representation data from the visibility calculations, and therefore, makes the method suitable for both primitive-order or screen-order rendering, such as deferred rendering. The sampling mechanism can be used in any application that performs visibility queries or ray marching.