Scientific Visualization and Computer Graphics

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We conduct research in the following major areas. Please make a selection from the menu below to learn more about research in any of these areas. We also maintain an overview of externally funded projects. A poster with an overview of our work is also available.

  Scientific Visualization

We investigate how to visualize medical and biological data. Methodologies such as functional MRI (fMRI), multichannel EEG, and diffusion tensor imaging (DTI) are used to extract functional brain networks and pathways. DNA microarray measurements allow us to visualize regulatory gene networks. We employ insights from perception science to improve current visualization techniques.

  Information Visualization

Information visualization is used to represent abstract data (such as file system hierarchies, web site or database contents, telecommunication or biological networks, etc.) in a visual way using good visual metaphors and efficient interaction techniques. In our research we focus on the combination of scientific and information visualization techniques in various areas, such as visualization of biological networks or software visualization.

  Interactive Computer Graphics

We investigate how to allow intuitive interaction with rendered images, on large, touch-sensitive displays for single or multiple people. Such direct interaction allows users to be creative and achieve their goals without a limiting interface, and to adapt the graphics and visualizations to their specific needs.

  Non-Photorealistic and Illustrative Rendering

We combine techniques from computer graphics, visualization, and interactive systems and get inspiration from centuries of traditional illustration. This non-photorealistic rendering breaks free from the traditional (photo-)realism and generates images or animations that use abstraction, exaggeration, and stylization. This increases the freedom of expression and, thus, has great potential for visualization and illustration.

  Multiscale Shape Processing

Multiscale shape processing involves 2D and 3D shape segmentation, denoising, edge and feature detection, simplification, and matching done on several levels of detail. These operations can be effectively and efficiently supported by several classes of methods, such as skeletonization (or medial axis) computation, level sets, and partial differential equations.

  Geometric Modelling

Shape representation is a key ingredient in many research areas and applications, such as Computer Aided Design (CAD), Computer Graphics, but also Finite Elements (FEM) and Isogeometric Analysis (IgA). We focus mainly on representations of (smooth) surfaces of arbitrary manifold topology and on (locally) refinable structures based on splines and/or subdivision, and their use in various application areas. We also investigate methods for converting input meshes into smooth surfaces which can be efficiently rendered on modern GPUs.

  Vector Graphics

Vector graphics, as opposed to raster graphics, represent digital images in a scalable and resolution independent manner, and are based on so-called primitives. These primitives range from simple ones such as lines, circles, disks, and rectangles, to more complicated ones like diffusion curves. We focus on the gradient mesh primitive, available e.g. in Adobe Illustrator and Inkscape, and its extensions, including (gradient) meshes of arbitrary manifold topology, sharp transitions, and local refinement. ----------------------------------------------------------------------

  Optimization and Machine Learning for Visualization

Optimization and machine learning can address various visualization problems in a data-driven way. Our research uses such techniques to analyze data, identify informative visual mappings, and dynamically adapting visualization systems at runtime. ----------------------------------------------------------------------

  High Performance Visualization

Visualization algorithms can be computationally demanding. Parallel and distributed computing enable the investigation of large data sets at interactive rates. We also work on approaches reducing computational cost by explicitly considering characteristics of the human visual system (e.g., foveated rendering). Finally, we develop in situ visualization methods processing data while it is generated to address the issue of the rate of data generation exceeding the capability for permanent storage (e.g., on supercomputers). ----------------------------------------------------------------------

  Visualization of Multifield Data

Analyzing multiple fields reflecting changes in time, simulation or experiment configuration, variables, etc. requires efficient visual abstraction. We work on different forms of aggregate representations, e.g., reducing data in time via selection or space via contours. We work both on generic and dedicated visual representations addressing specific research questions in another field (e.g., to support the understanding of fracture processes, CO2 bubble storage, and flow in porous media).

!! Major research topics * Perception * Brain visualization * InfoVIS * Software Visualization * Multiscale skeletons * Interaction & Graphics * NPR (:cell:)Information Visualization
Information visualization is used to represent abstract data in a visual way. It differs from traditional scientific visualization in that the underlying data are not the result of some simulation or measurement of physical (often 3D) data. Examples are: file system hierarchies, contents of a web site or database, telecommunication or biological networks, etc. Finding good visual metaphors and efficient interaction techniques for such abstract data is a major challenge. In our research we focus on the combination of scientific and information visualization techniques in various areas, such as visualization of biological networks or software visualization.