DNC-NET: DUAL-NEIGHBOURHOOD CONSENSUS NETWORK FOR FEATURE MATCHING

Local feature matching is the core of many computer vision tasks. While dectetor-based methods lack repeatability and only consider small image regions, sparse feature matching methods is challenging in textureless scenes. This paper proposes a novel feature matching method based on dual-neighbourhood consensus, termed as DNC-Net. In DNC-Net, we build the connection between keypoints and feature maps, so that the matching can comprehensively consider the small and large image regions. Further, dual-neighbourhood consensus fully learn the consensus between keypoints and feature maps, which improve matching robustness especially in textureless scenes. Experiments on homography estimation, outdoor pose estimation and image matching show that the model is superior to other methods, and has achieved the most advanced results.