  Feature matching is crucial in visual localization, where 2D-3D
correspondence plays a major role in determining the accuracy of camera pose. A
sufficient number of well-distributed 2D-3D correspondences is essential for
accurate pose estimation due to noise. However, existing 2D-3D feature matching
methods rely on finding nearest neighbors in the feature space and removing
outliers using hand-crafted heuristics, which may lead to potential matches
being missed or the correct matches being filtered out. In this work, we
propose a novel method called Geometry-Aided Matching (GAM), which incorporates
both appearance information and geometric context to address this issue and to
improve 2D-3D feature matching. GAM can greatly boost the recall of 2D-3D
matches while maintaining high precision. We apply GAM to a new hierarchical
visual localization pipeline and show that GAM can effectively improve the
robustness and accuracy of localization. Extensive experiments show that GAM
can find more real matches than hand-crafted heuristics and learning baselines.
Our proposed localization method achieves state-of-the-art results on multiple
visual localization datasets. Experiments on Cambridge Landmarks dataset show
that our method outperforms the existing state-of-the-art methods and is six
times faster than the top-performed method. The source code is available at
https://github.com/openxrlab/xrlocalization.
