Binocular Dense Depth Reconstruction using Isotropy Constraint

From two calibrated perspective views of a scene we make a direct metric reconstruction. From assumptions of translational, rotational, and scale invariance of 3D space and camera models we deduce the priors needed for a Bayesian estimation. This means that the reconstruction is optimal in the sense of Bayesian estimation with assumptions of Gaussian uncorrelated image noise and no preferred position, direction, and scale in the scene. It is shown that depth discontinuities can be reconstructed and results are presented. The constraint induced by the assumption of isotropy is shown to be invariant under change of the extrinsic camera parameters. It is argued that relaxation algorithms created to solve the stereo correspondance problem by optimization of non-convex functionals in general will rely on initial estimates or bias towards a prede ned solution. We use a multi-scale GNC-like algorithm to nd a solution from the initial estimates.