  Homography estimation is an important task in computer vision applications,
such as image stitching, video stabilization, and camera calibration.
Traditional homography estimation methods heavily depend on the quantity and
distribution of feature correspondences, leading to poor robustness in
low-texture scenes. The learning solutions, on the contrary, try to learn
robust deep features but demonstrate unsatisfying performance in the scenes
with low overlap rates. In this paper, we address these two problems
simultaneously by designing a contextual correlation layer (CCL). The CCL can
efficiently capture the long-range correlation within feature maps and can be
flexibly used in a learning framework. In addition, considering that a single
homography can not represent the complex spatial transformation in
depth-varying images with parallax, we propose to predict multi-grid homography
from global to local. Moreover, we equip our network with a depth perception
capability, by introducing a novel depth-aware shape-preserved loss. Extensive
experiments demonstrate the superiority of our method over state-of-the-art
solutions in the synthetic benchmark dataset and real-world dataset. The codes
and models will be available at
https://github.com/nie-lang/Multi-Grid-Deep-Homography.
