Feature-Assisted Sparse to Dense Motion Estimation Using Geodesic Distances

Abstract

Large motion displacements in image sequences are still a problem for most motion estimation techniques. Progress in feature matching allows to establish robust correspondences between images for a sparse set of points. Recent works have attempted to use this sparse information to guide the dense motion field estimation. We propose to achieve this in an extended motion estimation framework, which integrates information about the geodesic distance to the sparse features. Results show that by considering a handful of these feature matches, the geodesic distance is able to propagate the information efficiently.