Srhen

Abstract

The crux of homography estimation is that the homography is characterized by the geometric correspondences between two related images rather than appearance features, which differs from typical image recognition tasks. Existing methods either decompose the task of homography estimation into several individual sub-problems and optimize them sequentially, or attempt to tackle it in an end-to-end manner by delegating the whole task to deep convolutional networks (CNNs). However, it is quite arduous for CNNs to learn the mapping function from appearance features of related images to the homography directly. In this paper, we propose to parse the geometric correspondences between related images explicitly to bridge the gap between deep appearance features and the homography. Furthermore, we propose a coarse-to-fine estimation framework to capture different scale of homography transformations and thus predict the homography in a stepwise-refining manner. Additionally, we propose a pyramidal supervision scheme to leverage an important prior concerning the homography estimation. Extensive experiments on two large-scale datasets demonstrate that our model advances the state-of-the-art performance significantly. CCS CONCEPTS • Computing methodologies → Matching.