Data physicalizations (3D printed terrain models, anatomical scans, or even abstract data) can naturally engage both the visual and haptic senses in ways that are difficult or impossible to do with traditional planar touch screens and even immersive digital displays. Yet, the rigid 3D physicalizations produced with today's most common 3D printers are fundamentally limited for data exploration and querying tasks that require dynamic input (e.g., touch sensing) and output (e.g., animation), functions that are easily handled with digital displays. We introduce a novel style of hybrid virtual + physical visualization designed specifically to support interactive data exploration tasks. Working toward a "best of both worlds" solution, our approach fuses immersive AR, physical 3D data printouts, and touch sensing through the physicalization. We demonstrate that this solution can support three of the most common spatial data querying interactions used in scientific visualization (streamline seeding, dynamic cutting places, and world-in-miniature visualization). Finally, we present quantitative performance data and describe a first application to exploratory visualization of an actively studied supercomputer climate simulation data with feedback from domain scientists.

As high-performance computing (HPC) moves towards the exascale era, large-scale scientific simulations are generating enormous datasets. Many techniques (e.g., in-situ methods, data sampling, and compression) have been proposed to help visualize these large datasets under various constraints such as storage, power, and energy. However, evaluating these techniques and understanding the trade-offs (e.g., performance, efficiency, and quality) remains a challenging task.To enable exploration of the design space across such trade-offs, we propose the Exploration Test Harness (ETH), an architecture for the early-stage exploration of visualization and rendering approaches, job layout, and visualization pipelines. ETH covers a broader parameter space than current large-scale visualization applications such as ParaView and VisIt. It also promotes the study of simulation-visualization coupling strategies through a data-centric approach, rather than requiring coupling with a specific scientific simulation code. Furthermore, with experimentation on an extensively instrumented supercomputer, we study more metrics of interest than was previously possible. Importantly, ETH will help to answer important what-if scenarios and trade-off questions in the early stages of pipeline development, helping scientists to make informed choices about how to best couple a simulation code with visualization at extreme scale.

Abstract Visualizing 3D vector fields is challenging because of occlusion problems and the difficulty of providing depth cues that adequately support the perception of direction of flow lines in 3D space. One of the depth cues that has proven most valuable for the perception of other kinds of 3D data, notably 3D networks and 3D point clouds, is structure‐from‐motion (also called the Kinetic Depth Effect); another powerful depth cue is stereoscopic viewing. We carried out an experiment of the perception of direction for short streamlines passing through a cutting plane. The conditions included viewing with and without structure‐from‐motion and with and without stereoscopic depth. Conditions also include comparing streamtubes to lines. The results show that for this particular task, stereo provided an effective depth cue, but structure‐from‐motion did not. Ringed streamtubes and streamcones provided good 3D direction information, even without stereoscopic viewing. We conclude with guidelines for viewing slices through vector fields.

We introduce Artifact-Based Rendering (ABR), a framework of tools, algorithms, and processes that makes it possible to produce real, data-driven 3D scientific visualizations with a visual language derived entirely from colors, lines, textures, and forms created using traditional physical media or found in nature. A theory and process for ABR is presented to address three current needs: (i) designing better visualizations by making it possible for non-programmers to rapidly design and critique many alternative data-to-visual mappings; (ii) expanding the visual vocabulary used in scientific visualizations to depict increasingly complex multivariate data; (iii) bringing a more engaging, natural, and human-relatable handcrafted aesthetic to data visualization. New tools and algorithms to support ABR include front-end applets for constructing artifact-based colormaps, optimizing 3D scanned meshes for use in data visualization, and synthesizing textures from artifacts. These are complemented by an interactive rendering engine with custom algorithms and interfaces that demonstrate multiple new visual styles for depicting point, line, surface, and volume data. A within-the-research-team design study provides early evidence of the shift in visualization design processes that ABR is believed to enable when compared to traditional scientific visualization systems. Qualitative user feedback on applications to climate science and brain imaging support the utility of ABR for scientific discovery and public communication.

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.

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.

The scientific visualization community faces a crisis: there exist many individual tools that can be used to perform visualization, but there is little, if any, hope of being able to use tools from different sources as part of a single application. As a result, our community is fractured, and can be characterized as "islands of capability." The purpose of this panel is to probe the issues that prevent such interoperability, and engage in frank discussion about how our community can rectify these maladies. The issues to be discussed include but are not limited to: (1)lack of "standards" for data storage and modelling of N-dimensional scientific data, similar to those used for raster image files; (2)lack of "standard" interfaces for common visualization tools; (3)the visualization needs of the computational science research community, who are the primary consumers of technology from the visualization community; (4)lack of organization within our community to push for definition and adoption of such "standards;" (5)lack of organization within our community to serve as a "broker" and "promoter" for tools that might conform to even the weakest of standards. The panelist lineup represents a diverse cross-section of expertise and opinions about the panel topic. The panelists themselves are in disagreement about the severity of the problem, and potential solutions. The topic of this panel is highly germane to future growth of visualization as a science, and promises to be highly engaging for panelists and audience members alike.

Modular visualization environments utilizing a data-flow execution model have become quite popular in recent years, especially those that incorporate visual programming tools. However, simplistic implementations of such an execution model are quite limited when applied to problems of realistic complexity, which negate the intuitive advantage of data-flow systems. This situation can be resolved by extending the execution model to incorporate a more complete and efficient programming infrastructure while still preserving the virtues of pure "data-flow". This approach has been used for the implementation of a general-purpose software package, IBM Visualization Data Explorer.

This paper describes an implementation of Cook's "shade trees" in which shaders are described as networks of modules, building blocks, whose connections can be defined interactively.The high level interface to the shaders is a graphical editor which permits users to construct complex shaders by connecting shading elements in a network, in effect a graphical shading language. A low level interface to the shaders is also provided. In the low level interface, shading elements are programmed in a standard programming language and compiled into modules which can linked either at run time or compile time.Each link in the shading network represents a subroutine call. In essence, execution of the network is analogous to the execution of an interpreted language.

We demonstrate an efficient method of rendering alias-free synthetic images using precomputed convolution integrals. The method is based on the observation that a visible polygon fragment's contribution to an image is solely a function of its position and shape, and that within a reasonable level of accuracy, a limited number of shapes represent the majority of cases encountered in images commonly rendered.The convolution integral is precomputed for all pixels affected by the polygon fragment and is stored in a table. Completely visible fragments which are either triangular or trapezoidal produce two indices into the table. Most other fragments which are represented as differences of simple fragments. The remaining cases are characterized by a bit-mask for which each bit has a corresponding set of look up tables.The basic technique has been applied to several fundamentally different rendering algorithms. In addition, we illustrate a version of the newly introduced nonuniform sampling technique implemented in the same program, but with different table values.

Described is a visible surface algorithm and an implementation that generates shaded display of objects with hundreds of polygons rapidly enough for interactive use — several images per second. The basic algorithm, introduced in [Fuchs, Kedem and Naylor, 1980], is designed to handle rigid objects and scenes by preprocessing the object data base to minimize visibility computation cost. The speed of the algorithm is further enhanced by its simplicity, which allows it to be implemented within the internal graphics processor of a general purpose raster system.