Gibbs Probability Distributions for Stereo Reconstruction

A new approach for stereo reconstruction is proposed. This approach is based on a Gibbs probability distribution for surfaces in 3D space. The problem of stereo reconstruction is formulated then as a Bayes decision task. The main difference compared with known methods is the use of a more realistic cost function. In case of stereo reconstruction this function can be designed in some natural way, taking into account the properties of the surface model used. The proposed method solves the Bayes decision task approximately by a Gibbs Sampler. Learning of unknown distribution parameters is included as well, using the Expectation Maximization algorithm.