A Minimal Solution to the Generalized Pose-and-Scale Problem (with Jonathan Ventura, Clemens Arth and Dieter Schmalstieg)

We propose a novel solution to the generalized camera pose problem which includes the internal scale of the generalized camera as an unknown parameter. While a well-calibrated camera rig has a fixed and known scale, camera trajectories produced by monocular motion estimation necessarily lack a scale estimate. Thus, when performing loop closure in monocular visual odometry, or registering separate structure-from-motion reconstructions, we must estimate a seven degree-of-freedom similarity transform from corresponding observations. Our approach handles general configurations of rays and points and directly estimates the full similarity transformation from the 2D-3D correspondences. The minimal solver can be used in a hypothesize-and-test architecture for robust transformation estimation and produces a least-squares estimate in the overdetermined case.