A more reliable local-global-guided network for correspondence pruning

Abstract

The correspondence pruning task relies on both local and global contexts, which are considered to be essential in inferring the probability of inliers. Many previous approaches seek to devise various structures to make effective use of them, but they either use only a plain structure or base it on their own hypothetical relationships, which leads to some limitations remaining to be improved. Derived from this, we propose our LG-Net including a simple yet effective LGA block and a well-designed GPA block to extract local and global information respectively. Specifically, the LGA block combines local topology into the neighborhood and enhances the representative ability by enabling the interaction of the adjacency neighbors with a simple Softmax operation. Meanwhile, the GPA block prefers correspondences with higher inlier-probability to restrict the interference of outliers. With the guide of relatively reliable prior, it will facilitate the robustness of gathering rich global contextual information. As a consequence, our LG-Net takes both local and global context into account to help successfully recover correspondences. Extensive experiments have demonstrated the better performance of our method comparing with existing state-of-the-art methods on camera pose estimation and homography estimation.