Epipolar Geometry Estimation for Urban Scenes with Repetitive Structures.

Abstract

Algorithms for the estimation of epipolar geometry from a pair of images have been very successful in dealing with challenging wide baseline images. In this paper the problem of scenes with repeated structures is addressed, dealing with the common case where the overlap between the images consists mainly of facades of a building. These facades may contain many repeated structures that can not be matched locally, causing state-of-the-art algorithms to fail. Assuming that the repeated structures lie on a planar surface in an ordered fashion the goal is to match them. Our algorithm first rectifies the images such that the facade is fronto-parallel. It then clusters similar features in each of the two images and matches the clusters. From them a set of hypothesized homographies of the facade is generated, using local groups of features. For each homography the epipole is recovered, yielding a fundamental matrix. For the best solution, it then decides whether the fundamental matrix has been recovered reliably and, if not, returns only the homography. The algorithm has been tested on a large number of challenging image pairs of buildings from the benchmark ZuBuD database, outperforming several state-of-the-art algorithms.