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The focus of this course is on discussing efficient techniques to visually represent large-scale data sets from simulation and measurement. We will discuss the visualization pipeline, data structures, mapping techniques and special rendering techniques for data from different application domains such as fluid dynamics, climate research, medicine or biology. Various examples will be given to outline the benefits of visualization techniques in practical applications.

See some visualizations for yourself (these videos have been created with Amira by me and my colleagues at Zuse Institute Berlin):

Course topics:

  • Data Acquisition
  • Data Representation & Interpolation
  • Filtering techniques
  • Basic mapping techniques
  • Tree/Graph visualization
  • Multidimensional visualization
  • Volume Visualization
  • Flow Visualization
  • Feature Analysis
  • Topology


Inviwo is our programming framework for the practical tutorials. The software is open source (BSD).

Expected Work

The course is suitable for MS students. Familiarity with basic computer graphics (or motivation to learn this fast) is desirable. Assessment is based on weekly assignments and an exam at the end of the semester.


Practical assignments are done in group work (teams of three students) and consist of coding visualization algorithms within the GeoX framework. Grading is done using supervised peer review.

Theoretical assignments are done in individual work and cover the understanding of basic definitions, the execution of formulas, and occasionally a proof. Grading is done by the professor.

To be admitted to the exam, you need to have

  • Turned in 100% of all homework assignments. Yes, you need to work on all assignments.
  • Received at least 50% of all homework points.


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