Context Aware Terrain Visualization for Wayfinding and Navigation

To assist wayfinding and navigation, the display of maps and driving directions on mobile devices is nowadays commonplace. While existing system can naturally exploit GPS information to facilitate orientation, the inherently limited screen space is often perceived as a drawback compared to traditional street maps as it constrains the perception of contextual information. Moreover, occlusion issues add to this problem if the environment is shown from the popular egocentric perspective. In this paper we describe an interactive visualization system that addresses these problems by reallocating the available screen space. At the heart of our system are three novel visualization techniques: First, we propose a non‐standard perspective that allows to blend between the familiar pedestrian perspective and a standard map depiction with reduced occlusion. Second, we derive an efficient deformation technique that allows an interactive allocation of screen space to areas of interest like e.g. nearby touristic attractions. Finally, a path adaptive isometric perspective is proposed that reveals otherwise hidden facades in top‐down views. We describe efficient implementations of all techniques and exemplify our interactive system on real world urban models.     

Interactive Visualization of Function Fields by Range-Space Segmentation

We present a dimension reduction and feature extraction method for the visualization and analysis of function field data. Function fields are a class of high-dimensional, multi-variate data in which data samples are one-dimensional scalar functions. Our approach focuses upon the creation of high-dimensional range-space segmentations, from which we can generate meaningful visualizations and extract separating surfaces between features. We demonstrate our approach on high-dimensional spectral imagery, and particulate pollution data from air quality simulations.     

Discrete Multi‐Material Interface Reconstruction for Volume Fraction Data

Material interface reconstruction (MIR) is the task of constructing boundary interfaces between regions of homogeneous material, while satisfying volume constraints, over a structured or unstructured spatial domain. In this paper, we present a discrete approach to MIR based upon optimizing the labeling of fractional volume elements within a discretization of the problem's original domain. We detail how to construct and initially label a discretization, and introduce a volume conservative swap move for optimization. Furthermore, we discuss methods for extracting and visualizing material interfaces from the discretization. Our technique has significant advantages over previous methods: we produce interfaces between multiple materials that are continuous across cell boundaries for time‐varying and static data in arbitrary dimension with bounded error.     

Automatic Animation for Time‐Varying Data Visualization

This paper presents a digital storytelling approach that generates automatic animations for time‐varying data visualization. Our approach simulates the composition and transition of storytelling techniques and synthesizes animations to describe various event features. Specifically, we analyze information related to a given event and abstract it as an event graph, which represents data features as nodes and event relationships as links. This graph embeds a tree‐like hierarchical structure which encodes data features at different scales. Next, narrative structures are built by exploring starting nodes and suitable search strategies in this graph. Different stages of narrative structures are considered in our automatic rendering parameter decision process to generate animations as digital stories. We integrate this animation generation approach into an interactive exploration process of time‐varying data, so that more comprehensive information can be provided in a timely fashion. We demonstrate with a storm surge application that our approach allows semantic visualization of time‐varying data and easy animation generation for users without special knowledge about the underlying visualization techniques.     

A Gradient‐Based Comparison Measure for Visual analysis of Multifield Data

We introduce a multifield comparison measure for scalar fields that helps in studying relations between them. The comparison measure is insensitive to noise in the scalar fields and to noise in their gradients. Further, it can be computed robustly and efficiently. Results from the visual analysis of various data sets from climate science and combustion applications demonstrate the effective use of the measure.     

Visualization and Analysis‐Oriented Reconstruction of Material Interfaces

Reconstructing boundaries along material interfaces from volume fractions is a difficult problem, especially because the under‐resolved nature of the input data allows for many correct interpretations. Worse, algorithms widely accepted as appropriate for simulation are inappropriate for visualization. In this paper, we describe a new algorithm that is specifically intended for reconstructing material interfaces for visualization and analysis requirements. The algorithm performs well with respect to memory footprint and execution time, has desirable properties in various accuracy metrics, and also produces smooth surfaces with few artifacts, even when faced with more than two materials per cell.     

SDViz: A Context-Preserving Interactive Visualization System for Technical Diagrams

When performing daily maintenance and repair tasks, technicians require access to a variety of technical diagrams. As technicians trace components and diagrams from page-to-page, within and across manuals, the contextual information of the components they are analyzing can easily be lost. To overcome these issues, we have developed a Schematic Diagram Visualization System (SDViz) designed for maintaining and highlighting contextual information in technical documents, such as schematic and wiring diagrams. Our system incorporates various features to aid in the navigation and diagnosis of faults, as well as maintaining contextual information when tracing components/connections through multiple diagrams. System features include highlighting relationships between components and connectors, diagram annotation tools, the animation of flow through the system, a novel contextual blending method, and a variety of traditional focus+context visualization techniques. We have evaluated the usefulness of our system through a qualitative user study in which subjects utilized our system in diagnosing faults during a standard aircraft maintenance exercise.     

Caustic Forecasting: Unbiased Estimation of Caustic Lighting for Global Illumination

We present an unbiased method for generating caustic lighting using importance sampled Path Tracing with Caustic Forecasting. Our technique is part of a straightforward rendering scheme which extends the Illumination by Weak Singularities method to allow for fully unbiased global illumination with rapid convergence. A photon shooting preprocess, similar to that used in Photon Mapping, generates photons that interact with specular geometry. These photons are then clustered, effectively dividing the scene into regions which will contribute similar amounts of caustic lighting to the image. Finally, the photons are stored into spatial data structures associated with each cluster, and the clusters themselves are organized into a spatial data structure for fast searching. During rendering we use clusters to decide the caustic energy importance of a region, and use the local photons to aid in importance sampling, effectively reducing the number of samples required to capture caustic lighting.     

Stackless KD-Tree Traversal for High Performance GPU Ray Tracing

Significant advances have been achieved for realtime ray tracing recently, but realtime performance for complex scenes still requires large computational resources not yet available from the CPUs in standard PCs. Incidentally, most of these PCs also contain modern GPUs that do offer much larger raw compute power. However, limitations in the programming and memory model have so far kept the performance of GPU ray tracers well below that of their CPU counterparts. In this paper we present a novel packet ray traversal implementation that completely eliminates the need for maintaining a stack during kd-tree traversal and that reduces the number of traversal steps per ray. While CPUs benefit moderately from the stackless approach, it improves GPU performance significantly. We achieve a peak performance of over 16 million rays per second for reasonably complex scenes, including complex shading and secondary rays. Several examples show that with this new technique GPUs can actually outperform equivalent CPU based ray tracers.     

Physics-driven Multi Dimensional Keyframe Animation for Artist-directable Interactive Character

Various forms of art and entertainment involve many different characters, and advances in human interfaces have necessitated physical interactions in order to develop an improved sense of reality. In this paper we propose a method for generating the motions of characters using multidimensional keyframe animation in parallel with real-time physical simulation. The method generates characters capable of physical interaction, and also allows animators to use traditional methods for designing character motion. We have implemented the system and confirmed its effectiveness experimentally.     

Video Relighting Using Infrared Illumination

Inappropriate lighting is often responsible for poor quality video. In most offices and homes, lighting is not designed for video conferencing. This can result in unevenly lit faces, distracting shadows, and unnatural colors. We present a method for relighting faces that reduces the effects of uneven lighting and color. Our setup consists of a compact lighting rig and a camera that is both inexpensive and inconspicuous to the user. We use unperceivable infrared (IR) lights to obtain an illumination bases of the scene. Our algorithm computes an optimally weighted combination of IR bases to minimize lighting inconsistencies in foreground areas and reduce the effects of colored monitor light. However, IR relighting alone results in images with an unnatural ghostly appearance, thus a retargeting technique is presented which removes the unnatural IR effects and produces videos that have substantially more balanced intensity and color than the original video.     

Map Displays for the Analysis of Scalar Data on Cerebral Aneurysm Surfaces

Cerebral aneurysms result from a congenital or evolved weakness of stabilizing parts of the vessel wall and potentially lead to rupture and a life-threatening bleeding. Current medical research concentrates on the integration of blood flow simulation results for risk assessment of cerebral aneurysms. Scalar flow characteristics close to the aneurysm surface, such as wall shear stress, form an important part of the simulation results. Aneurysms exhibit variable surface shapes with only few landmarks. Therefore, the exploration and mental correlation of different surface regions is a difficult task. In this paper, we present an approach for the intuitive and interactive overview visualization of near wall flow data that is mapped onto the surface of a 3D model of a cerebral aneurysm. We combine a multi-perspective 2D projection map with a standard 3D visualization and present techniques to facilitate the correlation between a 3D model and a related 2D map. An informal evaluation with 4 experienced radiologists has shown that the map-based overview actually improves the surface exploration. Furthermore, different color schemes were discussed and, as a result, an appropriate color scheme for the visual analysis of the wall shear stress is presented.     

Hybrid Data Visualization Based on Depth Complexity Histogram Analysis

In many cases, only the combination of geometric and volumetric data sets is able to describe a single phenomenon under observation when visualizing large and complex data. When semi‐transparent geometry is present, correct rendering results require sorting of transparent structures. Additional complexity is introduced as the contributions from volumetric data have to be partitioned according to the geometric objects in the scene. The A‐buffer, an enhanced framebuffer with additional per‐pixel information, has previously been introduced to deal with the complexity caused by transparent objects. In this paper, we present an optimized rendering algorithm for hybrid volume‐geometry data based on the A‐buffer concept. We propose two novel components for modern GPUs that tailor memory utilization to the depth complexity of individual pixels. The proposed components are compatible with modern A‐buffer implementations and yield performance gains of up to eight times compared to existing approaches through reduced allocation and reuse of fast cache memory. We demonstrate the applicability of our approach and its performance with several examples from molecular biology, space weather and medical visualization containing both, volumetric data and geometric structures.     

Table‐driven Adaptive Importance Sampling

Monte Carlo rendering algorithms generally rely on some form of importance sampling to evaluate the measurement equation. Most of these importance sampling methods only take local information into account, however, so the actual importance function used may not closely resemble the light distribution in the scene. In this paper, we present Table‐driven Adaptive Importance Sampling (TAIS), a sampling technique that augments existing importance functions with tabular importance maps that direct sampling towards undersampled regions of path space. The importance maps are constructed lazily, relying on information gathered during the course of sampling. During sampling the importance maps act either in parallel with or as a preprocess to existing importance sampling methods. We show that our adaptive importance maps can be effective at reducing variance in a number of rendering situations.     

Survey of Real-Time Rendering Techniques for Crowds

Real‐time rendering of photo‐realistic humans is considerably outside the scope of current consumer‐level computer hardware. There are many techniques, which attempt to bridge the gap between what is desired and what is possible. This paper aims to give an overview of the techniques designed to alter the complexity of the model's geometry (level of detail), or replace it with a flat image (visual impostor) and to improve the lighting model (lighting and shadows). Recent years have shown a boom in the power and availability of consumer‐level programmable graphics processors, thus techniques that make use of these features are coming to the forefront.     

Energy Aware Color Sets

We present a design technique for colors with the purpose of lowering the energy consumption of the display device. Our approach is based on a screen space variant energy model. The result of our design is a set of distinguishable iso-lightness colors guided by perceptual principles. We present two variations of our approach. One is based on a set of discrete user-named (categorical) colors, which are analyzed according to their energy consumption. The second is based on the constrained continuous optimization of color energy in the perceptually uniform CIELAB color space. We quantitatively compare our two approaches with a traditional choice of colors, demonstrating that we typically save approximately 40 percent of the energy. The color sets are applied to examples from the 2D visualization of nominal data and volume rendering of 3D scalar fields.     

Fast Generation of Pointerless Octree Duals

Geometry processing applications frequently rely on octree structures, since they provide simple and efficient hierarchies for discrete data. However, octrees do not guarantee direct continuous interpolation of this data inside its nodes. This motivates the use of the octree's dual structure, which is one of the simplest continuous hierarchical structures. With the emergence of pointerless representations, with their ability to reduce memory footprint and adapt to parallel architectures, the generation of duals of pointerless octrees becomes a natural challenge. This work proposes strategies for dual generation of static or dynamic pointerless octrees. Experimentally, those methods enjoy the memory reduction of pointerless representations and speed up the execution by several factors compared to the usual recursive generation.     

Reusable Visualizations and Animations for Surgery Planning

For surgical planning, the exploration of 3D visualizations and 2D slice views is essential. However, the generation of visualizations which support the specific treatment decisions is very tedious. Therefore, the reuse of once designed visualizations for similar cases can strongly accelerate the process of surgical planning. We present a new technique that enables the easy reuse of both medical visualization types: 3D scenes and 2D slice views. We introduce the keystates as a concept to describe the state of a visualization in a general manner. They can be easily applied to new datasets to create similar visualizations. Keystates can be shared between surgeons of one specialization to reproduce and document the planning process for collaborative work. Furthermore, animations can support the surgeon on individual exploration and are also useful in collaborative environments, where complex issues must be presented in a short time. Therefore, we provide a framework, where animations can be visually designed by surgeons during their exploration process without any programming or authoring skills. We discuss several transitions between different visualizations and present an application from clinical routine.     

A Screen Space Quality Method for Data Abstraction

The rendering of large data sets can result in cluttered displays and non‐interactive update rates, leading to time consuming analyses. A straightforward solution is to reduce the number of items, thereby producing an abstraction of the data set. For the visual analysis to remain accurate, the graphical representation of the abstraction must preserve the significant features present in the original data. This paper presents a screen space quality method, based on distance transforms, that measures the visual quality of a data abstraction. This screen space measure is shown to better capture significant visual structures in data, compared with data space measures. The presented method is implemented on the GPU, allowing interactive creation of high quality graphical representations of multivariate data sets containing tens of thousands of items.     

Enclosing Surfaces for Point Clusters Using 3D Discrete Voronoi Diagrams

Point clusters occur in both spatial and non-spatial data. In the former context they may represent segmented particle data, in the latter context they may represent clusters in scatterplots. In order to visualize such point clusters, enclosing surfaces lead to much better comprehension than pure point renderings.
We propose a flexible system for the generation of enclosing surfaces for 3D point clusters. We developed a GPU-based 3D discrete Voronoi diagram computation that supports all surface extractions. Our system provides three different types of enclosing surfaces. By generating a discrete distance field to the point cluster and extracting an isosurface from the field, an enclosing surface with any distance to the point cluster can be generated. As a second type of enclosing surfaces, a hull of the point cluster is extracted. The generation of the hull uses a projection of the discrete Voronoi diagram of the point cluster to an isosurface to generate a polygonal surface. Generated hulls of non-convex clusters are also non-convex. The third type of enclosing surfaces can be created by computing a distance field to the hull and extracting an isosurface from the distance field. This method exhibits reduced bumpiness and can extract surfaces arbitrarily close to the point cluster without losing connectedness.
We apply our methods to the visualization of multidimensional spatial and non-spatial data. Multidimensional clusters are extracted and projected into a 3D visual space, where the point clusters are visualized. The respective clusters can also be visualized in object space when dealing with multidimensional particle data.