Correspondence selection aims to correctly select the consistent matches (inliers) from an initial set of putative correspondences. The selection is challenging since putative matches are typically extremely unbalanced, largely dominated by outliers, and the random distribution of such outliers further complicates the learning process for learning-based methods. To address this issue, we propose to progressively prune the correspondences via a local-to-global consensus learning procedure. We introduce a ``pruning'' block that lets us identify reliable candidates among the initial matches according to consensus scores estimated using local-to-global dynamic graphs. We then achieve progressive pruning by stacking multiple pruning blocks sequentially. Our method outperforms state-of-the-arts on robust line fitting, camera pose estimation and retrieval-based image localization benchmarks by significant margins and shows promising generalization ability to different datasets and detector/descriptor combinations.

翻译：相应的选择旨在从最初的一组假设通信中正确选择一致匹配( 直线) 。 选择具有挑战性, 因为假设匹配通常极不平衡, 主要由外部线主导, 而这种外部线的随机分布使学习方法的学习过程更加复杂。 为了解决这个问题, 我们提议通过一个本地到全球的共识学习程序逐步缩小对应关系。 我们引入一个“ 运行” 块, 使我们能够根据使用本地到全球动态图形估计的共识分数在初始匹配中识别可靠的候选人。 然后我们通过按顺序堆叠多个运行区块来逐步实现递减。 我们的方法在强的线安装、 相机构成估计和检索基于图像定位的基准上, 并显示有潜力的通用能力, 以不同的数据集和探测器/ 描述组合 。