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Scientific Visualisation 5hp

Learning Outcomes

To pass, the student should be able to

  • describe the data flow in a visualisation system;
  • outline the methods that transform the data and information to visual representations;
  • use and program advanced software for various visualisation techniques.


Scientific Visualisation is an area concerned with the visualization of large and complex data sets, where the data might come from experiments or computations. Visualisation is a way to achieve insight and knowledge of the data.
Discrete models. Volume rendering techniques. Iso-surface reconstruction. Mesh topologies and mesh simplification. Visualisation techniques. Visual aspects based on perception. Stereo techniques. Algorithms for programmable graphics hardware. Applied visualisation. The course includes projects such as programming in VTK (the Visualization Toolkit) using Python.


Lectures, laboratory work and compulsory project.


The students make a compulsory visualisation project at the end of the course, preferably using their own data. This project is reported not more than four weeks after the course ends.


Data Visualization: Principles and Practice (2nd edition, 610 pages),
Author: Alexandru C. Telea.