Stochastic stereo matching over scale

A stochastic optimization approach to stereo matching is presented. Unlike conventional correlation matching and feature matching, the method provides a dense array of disparities, eliminating the need for interpolation. First, the stereo-matching problem is defined in terms of finding a disparity map that satisfies two competing constraints: (1) matched points should have similar image intensity, and (2) the disparity map should vary as slowly as possible. These constraints are interpreted as specifying the potential energy of a system of oscillators. Ground states are approximated by a new variant of simulated annealing, which has two important features. First, the microcanonical ensemble is simulated using a new algorithm that is more efficient and more easily implemented than the familiar Metropolis algorithm (which simulates the canonical ensemble). Secondly, it uses a hierarchical, coarse-to-fine control structure employing Gaussian or Laplacian pyramids of the stereo images. In this way, quickly computed results at low resolutions are used to initialize the system at higher resolutions.Support for this work was provided by the Defense Advanced Research Projects Agency under contracts DCA 76-85-C-0004 and MDA 903-83-C-0084.