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Prof. Dr.-Ing. Tobias Günther

Department of Computer Science
Chair of Computer Science 9 (Computer Graphics)

Room: Room 01.115-128
Cauerstraße 11
91058 Erlangen

Bio
Since 2020, I am a professor of visual computing at the Friedrich-Alexander University of Erlangen-Nuremberg. Before, I was a postdoctoral researcher in the Computer Graphics Laboratory (CGL) at ETH Zurich, where I worked in the group of Prof. Markus Gross. In 2016, I received my Dr.-Ing. (Ph.D.) in the Visual Computing Group at the Otto-von-Guericke University of Magdeburg, advised by Prof. Holger Theisel.

Are you excited about graphics, machine learning and science? We have an open PhD position on machine learning in visualization and differentiable visualization. Contact tobias.guenther@fau.de for more information.

Eurovis 2021
The 23rd EG Conference on Visualization takes place in Zurich from 14-18 June 2021 and is organized by Prof. Renato Pajarola (University of Zurich) and Prof. Tobias Günther (FAU). Find out more at eurovis.org

 

 Jakob Jakob’s paper “A Fluid Flow Data Set for Machine Learning and its Application to Neural Flow Map Interpolation” was accepted at IEEE Visualization!

 Marco Ancona’s TVCG paper “MineTime Insight: Visualizing Meeting Habits to Promote Informed Scheduling Decisions” will be presented at IEEE Visualization!

 Ramon Witschi’s short paper “Implicit Ray Casting of the Parallel Vectors Operator” was accepted at IEEE Visualization!

 Marzieh Berenjkoub’s short paper “Vortex Boundary Identification using Convolutional Neural Network” was accepted at IEEE Visualization!

Feature Extraction in Unsteady Flows
Our research is dedicated to novel extraction and rendering techniques for the visualization of features in unsteady flows. For this, we apply techniques from light transport in heterogeneous participating media to the unbiased rendering of features in Lagrangian scalar fields. An example in atmospheric flows are the ridges of the finite-time Lyapunov exponent (FTLE), which constrain the advection of trace gases, guide temperature diffusion, and cloud formation.

Inertial Particle Dynamics
Recent research in flow visualization focused on the analysis of massless particles. However, in many application scenarios, the mass of particles and their resulting inertia are essential, for instance when sand particles interact with aircraft. The governing ordinary differential equation of even simple inertial flow models is up to seven dimensional, which makes feature extraction a challenging task. We extract and visualize integral geometry, study the vortical motion and separation behavior of inertial particles, and extend traditional vector field topology to the inertial case.

Optimal Reference Frames for Vortex Extraction
Vortex extraction is among the most challenging tasks of vector field analysis. We investigate elegant optimization-based approaches that extract vortices in an optimal near-steady reference frame. Vortex measures thereby become invariant under initial rotations and translations of the observer, i.e., they become objective.

Visibility Optimization
When it comes to 3D flow visualization, we often encounter occlusion problems when displaying dense sets of points, lines or multiple surfaces. A vital aspect is the careful selection of the primitives that best communicate the relevant features in a data set. We investigate optimization-based approaches that adjust the opacity of points, lines and surfaces to strive for a balance between the presentation of relevant information and occlusion avoidance.

Rendering
Depending on the degree of realism, the computation of photo-realistic images can take some time. We investigate techniques to accelerate Monte Carlo rendering in order to provide faster feedback and more control for artists. Further, we explore real-time rendering solutions that efficiently mimic natural phenomena, such as interactive material aging simulations.

Code can be found on the GitHub page of the group.
Data set are still hosted on my previous CGL website, but will move here soon.

Hauptseminar (HS)

Vorlesung (VORL)

Übung (UE)