DDFNet: Dual-Neighborhoods Dynamic Fusion Network for Image Feature Matching

Establishing reliable correspondences is a fundamental task in computer vision. Constructing neighbor graphs in feature space with position information to mine correspondence consistency has become a common strategy for recognizing correct correspondences (inliers). However, these neighbors may include a high ratio of incorrect correspondences (outliers), only using the correspondence consistency from feature space will probably be difficult to guarantee the matching accuracy. To address this issue, we propose a novel motion consistent space to find consistent neighbors that are independent of the correspondence's position and have a larger search range. On top of that, we build two neighbor graphs according to the feature space and motion consistent space separately, and expand a shift annular convolution to retain rich neighbor graph structure information and fully exploit the neighborhood context. Then, we design a dynamic feature fusion block to dynamically fuse these dual-neighbor graphs to flexibly cope with various complex scenarios. Finally, we develop a Dual-Neighborhoods Dynamic Fusion Network (DDFNet) for accurately identifying inliers and retrieving camera poses. Experimental results demonstrate that our proposed DDFNet outperforms the state-of-the-art methods. Source code:https://github.com/1211193023/DDFNet.