Using Rotation-Invariant Point and Line Features for Image Matching

In recent years, convolutional neural networks (CNNs) have outperformed traditional approaches in image matching tasks. However, suffering from poor robustness against object rotations, conventional CNNs tend to extract angle-specific feature representations from given images. To this end, group CNNs improve conventional CNNs with symmetric group theory and thus benefit their rotation equivariance for powerful feature learning. Nonetheless, how to enrich extracted features with better discriminability is an under-explored challenge for group CNNs. In this paper, we propose a powerful rotation-invariant image matching method that combines point and line features to jointly improve rotation equivariance and discriminability. Specifically, we first characterize richer features from images by detecting their keypoints and lines. Then, we employ a group convolutional backbone to extract rotation-invariant descriptors from detected keypoints and lines. Finally, we develop inter-image and intra-image attention strategies to integrate point-level and line-level features from two images, significantly facilitating the two-image matching task. Extensive experiments verify that our method achieves state-of-the-art matching accuracy among existing methods on varying rotation image datasets and also shows competitive results when transferred to real-world image matching.