Module Number

Module Title

Scientific Visualization
Type of Module

Elective Compulsory
Work load
- Contact time
- Self study
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency In the summer semester
Language of instruction English
Type of Exam

Oral examination (written exam if there are a large number of participants)

Lecture type(s) Lecture, Tutorial

The advances in modern high-performance computing and sensor technologies lead to increasingly large and complex data in many domains like the (life) sciences, medicine, physics, or engineering. Interactive visualization is often a crucial step to analyze these data. Scientific visualization is concerned with the depiction of data that has a spatial structure (mostly three-dimensional), for example medical volumes from CT or MRI scanners, or molecular structures. In this lecture, the steps of the visualization pipeline will be discussed, leading from the input data to the final image or the interactive rendering of the data set. This includes interpolation and filtering, mapping techniques, as well as the basics of (color) perception, computer graphics/rendering, and interaction. Visualization methods for different types of scientific data will be introduced, including particles, 3D scalar fields (volumes), vector fields, and tensor fields. In particular, the application of these methods for the visualization of biological as well as medical data will be discussed. This is, methods from Information Visualization (i.e., the visualization of abstract, non-spatial data) are not part of this lecture, as these are covered in BIO4364 - Visualization of Biological Data.


Students will
• know the most important concepts of scientific visualization (i.e., the graphical depiction of different types of spatial data, sampling, interpolation)
• know about visualization techniques for scientific data (e.g., particles, 3d scalar/vector/tensor fields)
• are able to implement these techniques on their own
• know how to apply scientific visualization effectively for the analysis of biological and medical data

Allocation of credits / grading
Type of Class
Type of Exam
Exam duration
of Module (%)
Prerequisite for participation MEINFM3142 Computer Graphics
Lecturer / Other Krone

Lecture slides will be made available for download.
C. Hansen, C. R. Johnson, “The Visualization Handbook,” Academic Press, 2005.
H. Schumann, W. Müller, “Visualisierung: Grundlagen und allgemeine Methoden,” Springer, 2000.
A. C. Telea, “Data Visualization: Principles and Practice,” A K Peters/CRC Press, 2nd edition, 2014.
M. O. Ward, G. Grinstein, D. Keim, “Interactive Data Visualization: Foundations, Techniques, and Applications,” CRC Press, 2nd edition, 2015.

Last offered unknown
Planned for Sommersemester 2024
Assigned Study Areas BIOINFM2510, INFM2510, INFM3110, MDZINFM2510, MEINFM3210, MEINFM3220