Before corresponding points in images taken with two cameras can be used to recover distances to objects in a scene, one has to determine the position and orientation of one camera relative to the other. This is the classic photogrammetric problem of relative orientation, central to the interpretation of binocular stereo information. Described here is a particularly simple iterative scheme for recovering relative orientation that, unlike existing methods, does not require a good initial guess for the baseline and the rotation.

This work investigates the process of automating reconstruction of buildings from video imagery. New metrics were developed to detect the least blurred images in a sequence for further processing. Phase correlation for point matching was investigated and new metrics were developed to identify successful matches. Direct relative orientation algorithms were investigated in-depth. A significant finding was a new 6-point algorithm which outperformed previously published algorithms for a number of calibrated camera and target geometries. The development of the new metrics and the outcomes from the comprehensive investigations conducted have contributed to a better understanding of the challenging problem of automatically reconstructing 3D objects from image sequences.