Deep Homography Estimation via Dense Scene Matching

Homography estimation is a fundamental task in many computer vision applications. Recently, deep learning-based methods have shown to be favorable for this problem when compared to traditional methods. They typically transform the homography estimation into regressing 4 point offsets which can be used to calculate a homography matrix. However, these methods usually use an implicit network to directly regress the offsets and completely ignores the matching information. The implicit regression network could be easy to cause overfitting and has limited representation ability to deal with image details. To address these problems, we propose an explicit dense scene matching strategy which introduces the matching information to establish a more rational connection between images and offsets. In this design, the network is only required to learn for simple feature encoding and information interaction tasks, which significantly reduces the dependence on representation ability of the network. As a result, the proposed explicit strategy can lead to better performance in aligning image details. Based on the dense scene matching, we propose a coarse-to-fine network consisting of three sub-models to progressively align images. Experimental results reveal that our method outperforms the state-of-the-art single-homography methods and multi-homography methods.