Previous methods solve feature matching and pose estimation using a two-stage
process by first finding matches and then estimating the pose. As they ignore
the geometric relationships between the two tasks, they focus on either
improving the quality of matches or filtering potential outliers, leading to
limited efficiency or accuracy. In contrast, we propose an iterative matching
and pose estimation framework (IMP) leveraging the geometric connections
between the two tasks: a few good matches are enough for a roughly accurate
pose estimation; a roughly accurate pose can be used to guide the matching by
providing geometric constraints. To this end, we implement a geometry-aware
recurrent attention-based module which jointly outputs sparse matches and
camera poses. Specifically, for each iteration, we first implicitly embed
geometric information into the module via a pose-consistency loss, allowing it
to predict geometry-aware matches progressively. Second, we introduce an
\textbf{e}fficient IMP, called EIMP, to dynamically discard keypoints without
potential matches, avoiding redundant updating and significantly reducing the
quadratic time complexity of attention computation in transformers. Experiments
on YFCC100m, Scannet, and Aachen Day-Night datasets demonstrate that the
proposed method outperforms previous approaches in terms of accuracy and
efficiency.

本文提出了一种基于几何关系的迭代匹配和位姿估计框架（IMP），它利用几何信息将匹配和位姿估计两个任务联系起来，并引入了一种几何感知模块（Geometry-aware recurrent attention-based module）和一种称为 EIMP 的有效 IMP 来提高算法的效率和准确性。实验结果表明，该方法在准确度和效率方面优于以往的方法。