Model-based segmentation and estimation of 3D surfaces from two or more intensity images using Markov random fields

An approach and algorithm for 3D primitive model recognition, parameter estimation, and segmentation from a sequence of images taken by one or more calibrated cameras are presented. Though the approach and algorithm are applicable to more general models, the experiments described are for primitive objects that are 3D planes. Given two or more images taken by one or more calibrated cameras, the algorithm simultaneously segments the images and 3D space into regions, each region associated with a single planar patch, and estimates the parameters of the 3D plane associated with each segmented region. The algorithm is suitable for parallel processing and should function at close to the best possible accuracy. Markov random fields are used to provide very coarse prior knowledge of the regions occupied by the planar patches, resulting in markedly enhanced accuracy