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.

翻译：在计算机视觉应用中,如图像缝合、视频稳定、相机校准等,测测同性恋是一项重要任务。传统的同系物估计方法在很大程度上取决于特征通信的数量和分布,导致低文本场景的强度差。相反,学习的解决方案试图学习强健的深度特征,但显示在低重叠率的场景中的表现不尽如人意。在本文中,我们同时通过设计一个背景相关层(CCL)来解决这两个问题。CCL可以有效地在地貌地图中捕捉到长距离的关联,并在一个学习框架中灵活使用。此外,考虑到单种同系物不能代表深盘图像的复杂空间变化,我们提议预测从全球到地方的多条形同体。此外,我们为我们的网络配备了深度感知能力,引入了一种新的深度觉识形状预设的损失。广泛的实验表明我们的方法优于综合基准数据集和真实世界数据集中的状态-艺术解决方案。代码和模型将在 https://githrib.G.comnie/langulmagraphy上提供。