AbHE: All Attention-Based Homography Estimation
Mingxiao Huo, Zhihao Zhang, Xinyang Ren, Xianqiang Yang, Chao Ye
Abstract
Homography estimation is a fundamental task in computer vision that involves obtaining the transformation between multi-view images for image alignment. Although convolutional neural networks (CNNs) have shown state-of-the-art performance in this task, few works have explored the use of transformer-based models, which have demonstrated superiority in high-level vision tasks. In this paper, we propose a strong baseline model for homography estimation that combines a swin transformer feature representation for global features and a CNN feature representation for local features. Additionally, we introduce a cross non-local layer to coarsely search for matched features within the feature maps. In the homography regression stage, we adopt an attention layer to drop out weak correlation feature points from the channels of the correlation volume. Our experiments show that our method outperforms the state-of-the-art methods in 8 Degree-of-Freedoms (DOFs) homography estimation. Code is available at https://github.com/mingxiaohuo/ABHE.