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.


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.