Teaching

Course Overview

This page contains a brief overview of the courses offered by the Professorship for Visual Computing. Further information can be found on the 🔗 Overview Page on StudOn.

Winter Semester

Physically-based Simulation in Computer Graphics (PhysSim)

Lecture: 4 SWS, 5.0 ECTS, English, Bachelor and Master

Visualization (Vis)

Lecture: 4 SWS, 5.0 ECTS, English, Bachelor and Master

Seminar Visual Computing (VCHS)

Seminar: 2 SWS, 5.0 ECTS, German and English, Master

Summer Semester

Scientific Visualization (SciVis)

Lecture: 4 SWS, 5.0 ECTS, English, Bachelor and Master

Interactive Visualization Project (VisPro)

Project: 8 SWS, 10.0 ECTS, English, Master

Virtual Courses

All lectures can be viewed virtually all year around and exams are offered at the end of each semester. To take a course virtually (or simply when you are interested in the course material), send your IdM to ✉ tobias.guenther@fau.de and ask for admittance into the respective StudOn course. Note that exercises are only provided in the respective semesters mentioned above.

Bachelor / Master Theses

If you are looking for a Bachelor or Master thesis topic, please see here for more information.

Course Details

Visualization (Vis, 93175)

Prof. Dr.-Ing. Tobias Günther, M.Sc. Xingze Tian

Lecture: 4 SWS, 5.0 ECTS, English, Bachelor and Master

An old English adage says "a picture is worth a 1,000 words", meaning that complex ideas are often easier to convey visually. This lecture is about the craft of creating informative images from data. Starting from the basics of the human visual perception, we will learn how visualizations are designed for explorative, communicative or confirmative purposes. We will see how data can be classified, allowing us to develop algorithms that apply to a wide range of application domains. The lecture covers the following topics:

  • data abstraction (data types, data set types, attribute types),
  • perception and mapping (marks and channels, effectiveness, pre-attentive vision, color maps),
  • task abstraction and validation (actions and targets),
  • information visualization tools (HTML, CSS, JavaScript, React, D3),
  • information visualization methods (tabular data, networks, trees),
  • scientific visualization methods (volume rendering and particle visualization),
  • scientific visualization tools (VTK, ParaView),
  • view manipulation (navigation, selection, multiple views),
  • data reduction (filtering, agreggation, focus and context),
  • lies in visualization (human biases and rules of thumb),
  • applications (deep learning, medical visualization, optimization)

The lecture is accompanied by exercises. Theoretical exercises concentrate on the classification of data and the design and analysis of visualizations, while programming exercises using web-based technologies give examples of their implementation. This course is taught in English.

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Scientific Visualization (SciVis, 43722)

Prof. Dr.-Ing. Tobias Günther, M.Sc. Xingze Tian

Lecture: 4 SWS, 5.0 ECTS, English, Bachelor and Master

The amount of data, generated in the pursuit of scientific discovery, keeps rapidly increasing across all major scientific disciplines. How can we make sense of large, time-dependent, high-dimensional and multi-variate data? This lecture provides an introduction into scientific visualization. Throughout the course, we cover the fundamental perception basics needed to convey information accurately. After categorizing different data types based on their dimensionality, we dive deeper into specific techniques for scalar, vector and tensor valued data. The lecture covers the following topics:

  • visualization design basics (data abstraction, visual encoding of information),
  • a review of scalar and vector calculus (differential properties, extremal and critical points),
  • data structures and data acquisition techniques (grids, interpolation, and differentiation),
  • indirect volume visualization (marching cubes and contour trees),
  • direct volume visualization (ray marching and Monte Carlo rendering),
  • elementary and line-based flow visualization (numerical integration, seeding, rendering),
  • surface-based flow visualization (integration, selection, rendering),
  • topology-based flow visualization (topological skeleton, bifurcations, feature flow fields),
  • feature-based flow visualization (vortices, material boundaries, Lagrangian coherent structures),
  • advanced methods (tensor visualization, uncertainty, ensembles)

The lecture is accompanied by voluntary exercises. Theoretical exercises concentrate on feature extraction from scalar and vector data, while programming exercises demonstrate the use of frameworks, such as the Visualization Tool Kit, to implement interactive scientific data visualizations. This course is taught in English.

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Interactive Visualization Project (VisPro, 93161)

Prof. Dr.-Ing. Tobias Günther

Project: 8 SWS, 10.0 ECTS, English, Master

Scientific computing is an essential building block in many scientific disciplines. Over the past years, the data complexity has increased rapidly in terms of size, dimensionality, and in the number of variables. In this group project, we will interactively visualize such scientific data sets, arising in fields such as climate science, cosmology, oceanology, fluid dynamics and geosciences.

In teams of 3-4 students, a scientific data set of choice is processed and visualized. Students are free to choose from a given set of data sets, including satellite measurements of volcanic eruptions, salt ensemble simulation, cosmology simulation, asteroid impact simulation, vortex street, weather simulation, ocean eddy simulation, Earth mantle convection, and wildfire simulation. During the first weeks, we develop a demo visualization system together, which will contain basic functionality such as direct and indirect volume rendering, as well as particle tracing. The group projects can be built on top of this demo system.

The group project is accompanied by a lecture, which covers implementation aspects of the following topics:

  • introduction to VTK and the programming framework (C++),
  • volume visualization (ray marching and marching cubes),
  • differential multi-variate calculus (scalar and vector fields, discretization),
  • numerical integration of particle trajectories (explicit integration schemes),
  • shader programming (for direct volume rendering),
  • image-based flow visualization (line integral convolutions),
  • vector field topology (critical point extraction and classification),
  • parallel vectors operator (feature definitions and numerical extraction),
  • vortex extraction (region-based and line-based techniques),
  • Lagrangian coherent structures (finite-time Lyapunov exponents),
  • reference frames (transformation and optimization),

This course is taught in English.

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