Feature matching is determining true correspondences between image pairs, important for many computer vision applications. It is challenging to determine true correspondences quickly under scene changes in viewpoint, rotation, scaling and illumination. Higher accuracy and efficiency are required for feature matching. While other methods determine true correspondences by treating the images independently, we instead condition on image pairs to take account of the affine information between them. To achieve this, we propose AAM-ORB, an efficient and robust algorithm for feature matching in the scene-shift. The key to our approach is an affine attention module (AAM), which can condition the affine features on both images to boost robustness. AAM is integrated into the well-known ORB feature matching pipeline, resulting in a significant improvement. Although remarkably matching accuracy, AAM can reduce computation efficient. To overcome this, we select a grid-based motion statistics for separating true correspondences from false ones at high speed. Extensive experiments show that AAM-ORB surpasses state-of-the-art approaches for feature matching on benchmark datasets. Moreover, the proposed AAM-ORB has less time consumption. Finally, AAM-ORB achieves better performance and efficiency of feature matching under scene changes.
