Modeling and Visualization of Uncertainty-Aware Geometry Using Multi-variate Normal Distributions
Christina Gillmann, Thomas Wischgoll, Bernd Hamann, James Paul Ahrens · 2018
Many applications are dealing with geometric data that are affected by uncertainty. This uncertainty is important to analyze, visualize, and understand. We present a methodology to model uncertain geometry based on multi-variate normal distributions. In addition, we propose a visualization technique to represent a hull for uncertain geometry capturing a user-defined percentage of the underlying uncertain geometry. To show the effectiveness of our approach, we have modeled and visualized uncertain datasets from different applications.