Stabilizing Stereo Correspondence Computation Using Delaunay Triangulation and Planar Homography

Abstract

A method for stabilizing the computation of stereo correspondences is presented in this paper. Delaunay triangulation is employed to partition the input images into small, localized regions. Instead of simply assuming that the surface patches viewed from these small triangles are locally planar, we explicitly examine the planarity hypothesis in the 3D space. To perform the planarity test robustly, adjacent triangles are merged into larger polygonal patches first and then the planarity assumption is verified. Once piece-wise planar patches are identified, point correspondences within these patches are readily computed through planar homographies. These point correspondences established by planar homographies serve as the ground control points (GCPs) in the final dynamic programming (DP)-based correspondence matching process. Our experimental results show that the proposed method works well on real indoor, outdoor, and medical image data and is also more efficient than the traditional DP method.