Visualization and virtual environments (VEs) have been two interconnected parallel strands in visual computing for decades. Some VEs have been purposely developed for visualization applications, while many visualization applications are exemplary showcases in general-purpose VEs. Because of the development and operation costs of VEs, the majority of visualization applications in practice have yet to benefit from the capacity of VEs. In this paper, we examine this status quo from an information-theoretic perspective. Our objectives are to conduct cost-benefit analysis on typical VE systems (including augmented and mixed reality, theater-based systems, and large powerwalls), to explain why some visualization applications benefit more from VEs than others, and to sketch out pathways for the future development of visualization applications in VEs. We support our theoretical propositions and analysis using theories and discoveries in the literature of cognitive sciences and the practical evidence reported in the literatures of visualization and VEs.

Abstract Modern supercomputers enable increasingly large N‐body simulations using unstructured point data. The structures implied by these points can be reconstructed implicitly. Direct volume rendering of radial basis function (RBF) kernels in domain‐space offers flexible classification and robust feature reconstruction, but achieving performant RBF volume rendering remains a challenge for existing methods on both CPUs and accelerators. In this paper, we present a fast CPU method for direct volume rendering of particle data with RBF kernels. We propose a novel two‐pass algorithm: first sampling the RBF field using coherent bounding hierarchy traversal, then subsequently integrating samples along ray segments. Our approach performs interactively for a range of data sets from molecular dynamics and astrophysics up to 82 million particles. It does not rely on level of detail or subsampling, and offers better reconstruction quality than structured volume rendering of the same data, exhibiting comparable performance and requiring no additional preprocessing or memory footprint other than the BVH. Lastly, our technique enables multi‐field, multi‐material classification of particle data, providing better insight and analysis.

We present VASA, a visual analytics platform consisting of a desktop application, a component model, and a suite of distributed simulation components for modeling the impact of societal threats such as weather, food contamination, and traffic on critical infrastructure such as supply chains, road networks, and power grids. Each component encapsulates a high-fidelity simulation model that together form an asynchronous simulation pipeline: a system of systems of individual simulations with a common data and parameter exchange format. At the heart of VASA is the Workbench, a visual analytics application providing three distinct features: (1) low-fidelity approximations of the distributed simulation components using local simulation proxies to enable analysts to interactively configure a simulation run; (2) computational steering mechanisms to manage the execution of individual simulation components; and (3) spatiotemporal and interactive methods to explore the combined results of a simulation run. We showcase the utility of the platform using examples involving supply chains during a hurricane as well as food contamination in a fast food restaurant chain.

Currently, user centered transfer function design begins with the user interacting with a one or two-dimensional histogram of the volumetric attribute space. The attribute space is visualized as a function of the number of voxels, allowing the user to explore the data in terms of the attribute size/magnitude. However, such visualizations provide the user with no information on the relationship between various attribute spaces (e.g., density, temperature, pressure, x, y, z) within the multivariate data. In this work, we propose a modification to the attribute space visualization in which the user is no longer presented with the magnitude of the attribute; instead, the user is presented with an information metric detailing the relationship between attributes of the multivariate volumetric data. In this way, the user can guide their exploration based on the relationship between the attribute magnitude and user selected attribute information as opposed to being constrained by only visualizing the magnitude of the attribute. We refer to this modification to the traditional histogram widget as an abstract attribute space representation. Our system utilizes common one and two-dimensional histogram widgets where the bins of the abstract attribute space now correspond to an attribute relationship in terms of the mean, standard deviation, entropy, or skewness. In this manner, we exploit the relationships and correlations present in the underlying data with respect to the dimension(s) under examination. These relationships are often times key to insight and allow us to guide attribute discovery as opposed to automatic extraction schemes which try to calculate and extract distinct attributes a priori. In this way, our system aids in the knowledge discovery of the interaction of properties within volumetric data.

Display Cluster is an interactive visualization environment for cluster-driven tiled displays. It provides a dynamic, desktop-like windowing system with built-in media viewing capability that supports ultra high-resolution imagery and video content and streaming that allows arbitrary applications from remote sources (such as laptops or remote visualization machines) to be shown. This support extends to high-performance parallel visualization applications, enabling interactive streaming and display for hundred-mega pixel dynamic content. Display Cluster also supports multi-user, multi-modal interaction via devices such as joysticks, smart phones, and the Microsoft Kinect. Further, our environment provides a Python-based scripting interface to automate any set of interactions. In this paper, we describe the features and architecture of Display Cluster, compare it to existing tiled display environments, and present examples of how it can combine the capabilities of large-scale remote visualization clusters and high-resolution tiled display systems. In particular, we demonstrate that Display Cluster can stream and display up to 36 mega pixels in real time and as many as 144 mega pixels interactively, which is 3× faster and 4× larger than other available display environments. Further, we achieve over a gig pixel per second of aggregate bandwidth streaming between a remote visualization cluster and our tiled display system.

In many scientific simulations, the temporal variation and analysis of features are important. Visualization and visual analysis of time series data is still a significant challenge because of the large volume of data. Irregular and scattered time series data sets are even more problematic to visualize interactively. Previous work proposed functional representation using basis functions as one solution for interactively visualizing scattered data by harnessing the power of modern PC graphics boards. In this paper, we use the functional representation approach for time-varying data sets and develop an efficient encoding technique utilizing temporal similarity between time steps. Our system utilizes a graduated approach of three methods with increasing time complexity based on the lack of similarity of the evolving data sets. Using this system, we are able to enhance the encoding performance for the time-varying data sets, reduce the data storage by saving only changed or additional basis functions over time, and interactively visualize the time-varying encoding results. Moreover, we present efficient rendering of the functional representations using binary space partitioning tree textures to increase the rendering performance.

We have developed an intuitive method to semiautomatically explore volumetric data in a focus-region-guided or value-driven way using a user-defined ray through the 3D volume and contour lines in the region of interest. After selecting a point of interest from a 2D perspective, which defines a ray through the 3D volume, our method provides analytical tools to assist in narrowing the region of interest to a desired set of features. Feature layers are identified in a 1D scalar value profile with the ray and are used to define default rendering parameters, such as color and opacity mappings, and locate the center of the region of interest. Contour lines are generated based on the feature layer level sets within interactively selected slices of the focus region. Finally, we utilize feature-preserving filters and demonstrate the applicability of our scheme to noisy data.

Abstract Multi‐dimensional transfer functions are commonly used in rectilinear volume renderings to effectively portray materials, material boundaries and even subtle variations along boundaries. However, most unstructured grid rendering algorithms only employ one‐dimensional transfer functions. This paper proposes a novel pre‐integrated Projected Tetrahedra (PT) rendering technique that applies bivariate transfer functions on unstructured grids. For each type of bivariate transfer function, an analytical form that pre‐integrates the contribution of a ray segment in one tetrahedron is derived, and can be precomputed as a lookup table to compute the color and opacity in a projected tetrahedron on‐the‐fly. Further, we show how to approximate the integral using the pre‐integration method for faster unstructured grid rendering. We demonstrate the advantages of our approach with a variety of examples and comparisons with one‐dimensional transfer functions.

A stand-alone visualization application has been developed by a multi-disciplinary, collaborative team with the sole purpose of creating an interactive exploration environment allowing turbulent flow researchers to experiment and validate hypotheses using visualization. This system has specific optimizations made in data management, caching computations, and visualization allowing for the interactive exploration of datasets on the order of 1TB in size. Using this application, the user (co-author Calo) is able to interactively visualize and analyze all regions of a transitional flow volume, including the laminar, transitional and fully turbulent regions. The underlying goal of the visualizations produced from these transitional flow simulations is to localize turbulent spots in the laminar region of the boundary layer, determine under which conditions they form, and follow their evolution. The initiation of turbulent spots, which ultimately lead to full turbulence, was located via a proposed feature detection condition and verified by experimental results. The conditions under which these turbulent spots form and coalesce are validated and presented.

Abstract Functional approximation of scattered data is a popular technique for compactly representing various types of datasets in computer graphics, including surface, volume, and vector datasets. Typically, sums of Gaussians or similar radial basis functions are used in the functional approximation and PC graphics hardware is used to quickly evaluate and render these datasets. Previously, researchers presented techniques for spatially‐limited spherical Gaussian radial basis function encoding and visualization of volumetric scalar, vector, and multifield datasets. While truncated radially symmetric basis functions are quick to evaluate and simple for encoding optimization, they are not the most appropriate choice for data that is not radially symmetric and are especially problematic for representing linear, planar, and many non‐spherical structures. Therefore, we have developed a volumetric approximation and visualization system using ellipsoidal Gaussian functions which provides greater compression, and visually more accurate encodings of volumetric scattered datasets. In this paper, we extend previous work to use ellipsoidal Gaussians as basis functions, create a rendering system to adapt these basis functions to graphics hardware rendering, and evaluate the encoding effectiveness and performance for both spherical Gaussians and ellipsoidal Gaussians. Categories and Subject Descriptors (according to ACMCCS): I.3.3 [Computer Graphics]: Scientific Visualization, Ellipsoidal Basis Functions, Functional Approximation, Texture Advection

Understanding and analyzing complex volumetrically varying data is a difficult problem. Many computational visualization techniques have had only limited success in succinctly portraying the structure of three-dimensional turbulent flow. Motivated by both the extensive history and success of illustration and photographic flow visualization techniques, we have developed a new interactive volume rendering and visualization system for flows and volumes that simulates and enhances traditional illustration, experimental advection, and photographic flow visualization techniques. Our system uses a combination of varying focal and contextual illustrative styles, new advanced two-dimensional transfer functions, enhanced Schlieren and shadowgraphy shaders, and novel oriented structure enhancement techniques to allow interactive visualization, exploration, and comparative analysis of scalar, vector, and time-varying volume datasets. Both traditional illustration techniques and photographic flow visualization techniques effectively reduce visual clutter by using compact oriented structure information to convey three-dimensional structures. Therefore, a key to the effectiveness of our system is using one-dimensional (Schlieren and shadowgraphy) and two-dimensional (silhouette) oriented structural information to reduce visual clutter, while still providing enough three-dimensional structural information for the user's visual system to understand complex three-dimensional flow data. By combining these oriented feature visualization techniques with flexible transfer function controls, we can visualize scalar and vector data, allow comparative visualization of flow properties in a succinct, informative manner, and provide continuity for visualizing time-varying datasets.

The evolution of computational science over the last decade has resulted in a dramatic increase in raw problem solving capabilities. This growth has given rise to advances in scientific and engineering simulations that have put a high demand on tools for high-performance large-scale data exploration and analysis. These simulations have the potential to generate large amounts of data. Humans, however are relatively poor at gaining insight from raw numerical data, and as a result, have used visualization as a tool for understanding, interpreting and exploring data of all types and sizes. Allowing for efficient visual explorations of data, however, requires that the ratio of knowledge gained versus the cost of the visualization be maximized. This, in turn, mandates the integration of principles from human perception. Understanding perception as it relates to visualization requires that we understand not only the biology of the human visual system, but principles from vision theory, and perceptual psychology as well. This panel is the result of bringing together practioners and researchers from a broad spectrum of interests relating to the ability to maximize the amount of information that is effectively perceived from a given visualization. Position statements will be given by researchers interested in perceptual psychology and the perception of natural images, integrating art and design principles, non-photorealistic rendering techniques, and the use of global illumination methods to provide benefical perceptual cues.

Introduction Shown in Figure 1 is the simulation of the energetics of the dissociation of a metastable molecule. The simulation was computed by Dr. Robert Wyatt, a Professor of Chemistry at The University of Texas at Austin. Dr. Wyatt’s simulation uses a model molecule composed of two fragments, A and B. Starting from the molecular state AB, the simulation computes the probability that, under any given conditions, AB will dissociate into the separated components A + B. This may seem like a toy problem, but in real life many molecules demonstrate metastable behavior--a propensity to dissociate--and the work that this simulation is doing may ultimately have an impact on studies of the energetics of larger molecules. An important class of these is biomolecular. This work may be particularly applicable to energy transfer problems in biological systems.

Introduction The images shown in Figure 1 display a single time step of a turbulent flow simulation computed by Dr. Thomas J. R. Hughes, Professor of Aerospace Engineering, and Victor Calo at The University of Texas at Austin. This research examines how a fluid running over a flat plate suddenly becomes turbulent. The smooth flow seen at the left-hand side of the volume has low-amplitude fluctuations that interact with the smooth, laminar boundary layer of the fluid. As the energy of the fluctuations is convected downward into the boundary layer, there is a sudden explosion of the entire flow into turbulence. The left-most image of Figure 1 displays the left to right nature of the fluid as it passes over the flat plate, showing the turbulent boundary layer. The middle image shows the volumetric flow with sheet of particles inserted at designated locations to show increasing turbulent behavior. The right-most image in Figure 1 is a close up of particle sheets showing local velocity fluctuations inside the turbulent boundary layer.

Introduction Reconstructing the evolutionary history of diverse species is a basic goal of systematic biology and is essential to comparative biology. Phylogenies representing this history are constructed from the analyses of molecular or morphological data and used as tools for understanding the evolution of complex traits. Objectives of this research are to create visualization tools that dynamically reconstruct horn morphology in three-dimensions (3D) for horned lizards and show how changes occur along a phylogeny via metamorphosis (morphing). These objectives will be met by incorporating results from phylogenetic analyses with ancestral trait reconstructions applied to 3-D images generated from a highresolution x-ray computed tomography (CT) scanner. Ancestral reconstruction algorithms are incorporated with nonlinear morphing to accommodate different rates of change along and between lineages. The application of 3-D morphing to systematics and ancestral reconstruction is a novel approach in visualization methods and opens a new realm of collaborative learning and discovery. We believe these tools will provide a greater understanding of how complex traits have evolved by allowing scientists to peer into the past, to see what ancestral states look like, and to visualize changes through time.

Visualization of computational oceanography is traditionally a post-processing step. This batch orientation is clumsy if one wants to observe the effect of a wide range of parameters on the solution. This paper describes the conversion of an ocean circulation model from this traditional design to an interactive program in which the computed solution is viewed in real-time over a wide-area network and the user is given the ability to change the model parameters and immediately observe the impact this has on the solution.