Homography estimation is a basic computer vision task, which aims to obtain the transformation from multi-view images for image alignment. Unsupervised learning homography estimation trains a convolution neural network for feature extraction and transformation matrix regression. While the state-of-theart homography method is based on convolution neural networks, few work focuses on transformer which shows superiority in highlevel vision tasks. In this paper, we propose a strong-baseline model based on the Swin Transformer, which combines convolution neural network for local features and transformer module for global features. Moreover, a cross non-local layer is introduced to search the matched features within the feature maps coarsely. In the homography regression stage, we adopt an attention layer for the channels of correlation volume, which can drop out some weak correlation feature points. The experiment shows that in 8 Degree-of-Freedoms(DOFs) homography estimation our method overperforms the state-of-the-art method.

翻译：单应性估计是一项基础的计算机视觉任务，旨在从多视角图像中获取变换关系以实现图像对齐。无监督学习的单应性估计方法通过训练卷积神经网络进行特征提取和变换矩阵回归。尽管当前最先进的单应性方法基于卷积神经网络，但鲜有研究关注在高层次视觉任务中表现优越的Transformer。本文提出一个基于Swin Transformer的强基线模型，该模型结合卷积神经网络提取局部特征与Transformer模块提取全局特征。此外，我们引入跨非局部层以粗略搜索特征图内的匹配特征。在单应性回归阶段，我们采用注意力层处理相关体积的通道，能够丢弃部分弱相关性特征点。实验表明，该方法在8自由度单应性估计中优于当前最先进方法。