Scalable Comparative Visualization

Master Thesis


The field of Comparative Visualization struggles with finding effective ways to compare multiple visual elements to one another. For example, it addresses the problem of trying to understand how multiple ensembles of contours or 3D meshes differ from each other. Although many approaches have been proposed [1], most of do not tackle appropriately scalability, i.e., when the number of elements to compare increases, the method does not perform well. This becomes even worse, when we have 4D data, e.g. 3D meshes changing through time.


The task of this project is to design and implement scalable, expressive and intuitive strategies for the comparative visualization of 3D and 4D data. These strategies will be based on state-of-the-art methods from the domain of Comparative Visualization and Visual Analytics. More details about the subtasks and the workflow of the topic will be provided upon request.


  • Knowledge of English language (source code comments and final report should be in English)
  • Interest and Knowledge in Medical Visualization, in particular Comparative Visualization and Visual Analytics
  • Good programming skills
  • Creativity and enthusiasm


The project should be implemented as a standalone application, desktop or web-based (tools to be discussed).

Further Reading

[1] Comparison techniques utilized in spatial 3D and 4D data visualizations: A survey and future directions - Kim et al., 2017

[2] YMCA: Your Mesh Comparison Algorithm - Schmidt et al., 2014

[3] Time-hierarchical clustering and visualization of weather forecast ensembles - Ferstl et al., 2017

[4] Pelvis Runner - Grossmann et al., 2019


For more information please contact Renata Raidou.