I'm a doctoral researcher at the University of Stuttgart Visualization Research Center (VISUS) and part of the collaborative research center SFB-TRR 161 on quantitative methods for visual computing. I develop new methods to assess, model, and predict performance of visual computing systems. Thereby, I focus on applications from the domain of scientific visualization.
M.Sc. in Computer Science, 2016
University of Stuttgart, Germany
B.Sc. in Computer Science, 2013
Osnabrück University, Germany
Foveal vision is located in the center of the field of view with a rich impression of detail and color, whereas peripheral vision occurs on the side with more fuzzy and colorless perception. This visual acuity fall-off can be used to achieve higher frame rates by adapting rendering quality to the human visual system. Volume raycasting has unique characteristics, preventing a direct transfer of many traditional foveated rendering techniques. We present an approach that utilizes the visual acuity fall-off to accelerate volume rendering based on Linde-Buzo-Gray sampling and natural neighbor interpolation. First, we measure gaze using a stationary 1200 Hz eye-tracking system. Then, we adapt our sampling and reconstruction strategy to that gaze. Finally, we apply a temporal smoothing filter to attenuate undersampling artifacts since peripheral vision is particularly sensitive to contrast changes and movement. Our approach substantially improves rendering performance with barely perceptible changes in visual quality. We demonstrate the usefulness of our approach through performance measurements on various data sets.
As our field matures, evaluation of visualization techniques has extended from reporting runtime performance to studying user behavior. Consequently, many methodologies and best practices for user studies have evolved. While maintaining interactivity continues to be crucial for the exploration of large data sets, no similar methodological foundation for evaluating runtime performance has been developed. Our analysis of 50 recent visualization papers on new or improved techniques for rendering volumes or particles indicates that only a very limited set of parameters like different data sets, camera paths, viewport sizes, and GPUs are investigated, which make comparison with other techniques or generalization to other parameter ranges at least questionable. To derive a deeper understanding of qualitative runtime behavior and quantitative parameter dependencies, we developed a framework for the most exhaustive performance evaluation of volume and particle visualization techniques that we are aware of, including millions of measurements on ten different GPUs. This paper reports on our insights from statistical analysis of this data, discussing independent and linear parameter behavior and non-obvious effects. We give recommendations for best practices when evaluating runtime performance of scientific visualization applications, which can serve as a starting point for more elaborate models of performance quantification.
Presentation of our Transactions on Visualization and Computer Graphics paper.
Presentation of our ETRA 2019 paper.
Presentation of our EuroVis 2019 short paper.
Early Career Program Committee Member, In Situ Infrastructures for Enabling Extreme-scale Analysis and Visualization (ISAV), 2019 and 2020.
I reviewed for IEEE VIS, EuroVis, LDAV, Computers & Graphics, EGPGV, ETRA, and Future Generation Computer Systems.
|Foveated volume rendering||Bachelor|
|Generating field data with GANs for evaluation of visualization performance||Master|
|Encoding quality prediction for interactive remote visualization||Master|
|Investigation of volume rendering performance through active learning and visual analysis||Master|
|GPU-accelerated rendering of dense light fields||Research Module|
|Power efficiency for volume rendering on mobile devices||Research Module|
|Investigation and prediction of performance of distributed volume rendering||Master|
|In Situ Visualization||Seminar||Summer 2020|
|Advanced Rendering||Advanced seminar||Winter 2019 / 20|
|Realistic Real-Time Graphics||Seminar||Summer 2019|
|Computer Graphics||Lecture||Winter 2018 / 19|
|Computer Graphics||Lecture||Winter 2017 / 18|
|Image Synthesis||Lecture||Summer 2017|
|Practical Course Visual Computing||Lab||Winter 2016 / 17|
|Image Synthesis||Lecture||Summer 2016|