Reliable point correspondences in scenes dominated by highly reflective and largely homogeneous surfaces

Abstract

Common Structure from Motion (SfM) tasks require reliable point correspondences in images taken from different views to subsequently estimate model parameters which describe the 3D scene geometry. For example when estimating the fundamental matrix from point correspondences using RANSAC. The amount of noise in the point correspondences drastically affect the estimation algorithm and the number of iterations needed for convergence grows exponentially with the level of noise. In scenes dominated by highly reflective and largely homogeneous surfaces such as vehicle panels and buildings with a lot of glass, existing approaches give a very high proportion of spurious point correspondences. As a result the number of iterations required for subsequent model estimation algorithms become intractable. We propose a novel method that uses descriptors evaluated along points in image edges to obtain a sufficiently high proportion of correct point correspondences. We show experimentally that our method gives better results in recovering the epipolar geometry in scenes dominated by highly reflective and homogeneous surfaces compared to common baseline methods on stereo images taken from considerably wide baselines.