Tasks such as image registration or pose estimation require the determination of transformations based on uncertain observations. Hence, the position of any geometric object transformed according to this estimate is also uncertain, at least in terms of precision. Often the knowledge of uncertainty changes the judgment of individuals. Thus, the visualization of this information is crucial whenever a human decision-maker is involved. In the absence of error-free reference data, we consider the estimated precision as the probably most important quantity characterizing the uncertainty. This contribution focuses on the visualization of positional precision as provided by estimated covariance matrices. Basic design principles such as coloration and contouring in 2D and 3D are presented and discussed in the context of practical applications, e.g., the superimposition of distance information as seen nowadays in sports broadcasts. As a novel contribution, we propose quartic plane curves to represent the confidence regions of the loci of conic sections.
