Visualizing multiple error-sensitivity fields for single camera positioning

Abstract

In many data acquisition tasks, the placement of a real camera can vary significantly in complexity from one scene to another. Optimal camera positioning should be governed not only by least error sensitivity, but in addition to real-world practicalities given by various physical, financial and other types of constraints. It would be a laborious and costly task to model all these constraints if one were to rely solely on fully automatic algorithms to make the decision. In this work, we present a study using 2D and 3D visualization methods to assist in single camera positioning based on error sensitivity of reconstruction and other physical and financial constraints. We develop a collection of visual mappings that depict the composition of multiple error sensitivity fields that occur for a given camera position. Each camera position is then mapped to a 3D visualization that enables visual assessment of the camera configuration. We find that the combined 2D and

Insights into the robustness of control point configurations for homography and planar pose estimation

“…Additionally, in [21], [22] evaluations are presented characterizing pose-dependent offsets and uncertainty on the camera pose estimations. It is empirically proven by simulations that some poses of the camera are more stable for the estimation process than others.…”