  We examine 3D reconstruction of architectural scenes in unordered sets of
uncalibrated images. We introduce a linear method to self-calibrate and find
the metric reconstruction of a camera pair. We assume unknown and different
focal lengths but otherwise known internal camera parameters and a known
projective reconstruction of the camera pair. We recover two possible camera
configurations in space and use the Cheirality condition, that all 3D scene
points are in front of both cameras, to disambiguate the solution. We show in
two Theorems, first that the two solutions are in mirror positions and then the
relations between their viewing directions. Our new method performs on par
(median rotation error $\Delta R = 3.49^{\circ}$) with the standard approach of
Kruppa equations ($\Delta R = 3.77^{\circ}$) for self-calibration and 5-Point
algorithm for calibrated metric reconstruction of a camera pair. We reject
erroneous image correspondences by introducing a method to examine whether
point correspondences appear in the same order along $x, y$ image axes in image
pairs. We evaluate this method by its precision and recall and show that it
improves the robustness of point matches in architectural and general scenes.
Finally, we integrate all the introduced methods to a 3D reconstruction
pipeline. We utilize the numerous camera pair metric recontructions using
rotation-averaging algorithms and a novel method to average focal length
estimates.
