Abstract
Homography estimation plays a pivotal role in aligning image pairs across multiple viewpoints. Existing methods focus mainly on texture alignment, whereas overlooking the influence of geometric structures, thereby resulting in inaccurate homography estimation. In this paper, we propose a novel recurrent homography estimation framework with joint edge detection learning. We find that edge detection explores extra anchors for homography estimation, and meanwhile homography provides complementary information of cross views for edge detection refinement. Unlike traditional edge detection applied to individual images, our approach establishes structural consistency constraints to reinforce mutual edges while suppressing unreliable structures. Specifically, the detected edges guide and enhance the texture features through a specifically designed edge-aware fusion module. Ultimately, we recurrently compute the correlation of fusion features from small to large scales for homography regression. Our experimental results demonstrate that the proposed method reduces the matching error by 41.7% than state-of-the-art methods. Furthermore, our network excels in detecting edges with extensive details even under dramatic perspective changes. Code is available at https://github.com/edmandzhao/edge-detection-for-RHE.