Learning priors for calibrating families of stereo cameras

Online camera recalibration is necessary for long-term deployment of computer vision systems. Existing algorithms assume that the source of recalibration information is a set of features in a general 3D scene; and that enough features are observed that the calibration problem is well-constrained. However; these assumptions are frequently invalid outside the laboratory. Real-world scenes often lack texture, contain repeated texture, or are mostly planar, making calibration difficult or impossible. In this paper we consider the calibration of families of stereo cameras, where each camera is assumed to have parameters drawn from a common but unknown prior distribution. We show how estimation of this prior using a small-number of offline-calibrated cameras (e.g. from the same production line) allows online calibration of additional cameras using a small number of point correspondences; and that using the estimated prior significantly increases the accuracy and robustness of stereo camera calibration.