Surfaces from Stereo: Integrating Feature Matching, Disparity Estimation, and Contour Detection

The goal of stereo algorithms is to determine the threedimensional distance, or depth, of objects from a stereo pair of images. The usual approach is to first identify corresponding features between the two images and estimate their depths, then interpolate to obtain a complete distance or depth map. Traditionally, finding the corresponding features has been considered to be the most difficult problem. Also, occluding and ridge contours (depth and orientation discontinuities) have not been explicitly detected which has made surface interpolation difficult. The approach described in this paper integrates the processes of feature matching, contour detection, and surface interpolation. Integration is necessary to ensure that the detected surfaces are smooth. Surface interpolation takes into account detected occluding and ridge contours in the scene; interpolation is performed within regions enclosed by these contours. Planar and quadratic patches are used as local models of the surface. Occluded regions in the image are identified, and are not used for matching and interpolation. A coarse-to-fine algorithm is presented that generates a multiresolution hierarchy of surface maps, one at each level of resolution. Experimental results are given for a variety of stereo images.