Scientific Visualization

Visualization pipeline (data acquisition, filtering, display), information visualization vs. scientific visualization, grids and grid construction (Delaunay triangulation), interpolation in grids (inverse distance weighting, radial basis functions), discretization aspects, visualization for scalar fields (color coding, iso-contours and iso-surfaces, volume rendering, vector field visualization (particle-based visualization).

After successful completion of the module, the students have gained advanced knwowledge concerning the visualization pipeline, ranging from data acquisition to the final image of this data. This includes knowledge about the application specific data representations, data interpolation and approximation techniques for discrete data sets, data filtering techniques like convolution, as well as the final mapping stage to generate a renderable representation from the data. The students know the methods which are used in scientific visualiztion to graphically depict 2D and 3D scalar and vector fields, including isocontouring, direct volume rendering and flow visualization. They can analyse and categorize availaible techniques in terms of quality, efficiency, and suitability for a particular data type, and they can model and develop new approaches considering application-specific requirements.

None

The modul consists of the lecture, where the lecturer conveys to the students the area-specific knowledge, points towards relevant articles and ecourages the students to read and put into relation the presented approaches, and gives examples demonstrating the application of these approaches. In some online demonstration the lecturer introduces state-of-the-art tools for scientific visualization.

Powerpoint course slides, white board exercises, online tutorials and demonstrations

Schumann, Müller: Visualisierung - Grundlagen und allgemeine Methoden, Springer Verlag
C. Hansen, C. Johnson (Ed.): The handbook of Visualization, Academic Press

The exam takes the form of a written test of 75 minutes.

The students demonstrate that they can answer questions concerning the theoretical and methodological foundations of scientific visualization. They know important application domains where visualization methods are used, and they are familiar with the application-specific data modalities a visualization person is confronted with. They also demonstrate the ability to apply the learned concepts and methods, such as the color mapping, iso-contouring or particle-tracing, to derive solutions for specific visualization problems. The exam captures all content discussed in the lecture.