Fire training for non-professionals is expensive, complicated and dangerous. The solution: a simulation in a virtual environment.
This project is dedicated to training and workflows in Mixed Reality.
To take full advantage of data from automotive simulations and measurements, we combine interactive and automatic Visual Computing methods to find intuitive, efficient and effective solutions that are applicable in the daily routine of engineers.
In this project we develop new methods from the field of visual analysis and machine learning to automate the quality control and quality assurance of glass articles.
In this project tools and methods for handling, administration, manipulation and evaluation of several different data sources for measurements and lighting design are developed.
Research project on powerful visualization methods to support decision making in complex infrastructure projects, especially in tunnel, railway and road construction.
MINERVA is an integrated framework for planetary scientists allowing members of different instrument teams to cooperate synergistically in virtual workspaces by sharing observations, analyses and annotations of heterogonous mission data.
Visualization and visual analysis of high-resolution surface reconstructions find a wide range of applications, from tunnel monitoring and archaeological excavations to the change management of cultural heritage buildings.
The long-term vision of this applied research project is to use available data resources to improve image-based diagnostics based on complex data in daily clinical routine.
The combination of "liquid biopsies", machine learning and data visualization aims to enable earlier and more accurate prediction of a relapse in children with cancer.
The strategic project forms the organizational and scientific hub for the realization of an area wide integrative visual computing approach. It covers joint strategic research and development on fundamental challenges in all application projects.
Complex simulation data is practically omnipresent today.