In this paper, we propose to go beyond the well-established approach to
vision-based localization that relies on visual descriptor matching between a
query image and a 3D point cloud. While matching keypoints via visual
descriptors makes localization highly accurate, it has significant storage
demands, raises privacy concerns and requires update to the descriptors in the
long-term. To elegantly address those practical challenges for large-scale
localization, we present GoMatch, an alternative to visual-based matching that
solely relies on geometric information for matching image keypoints to maps,
represented as sets of bearing vectors. Our novel bearing vectors
representation of 3D points, significantly relieves the cross-modal challenge
in geometric-based matching that prevented prior work to tackle localization in
a realistic environment. With additional careful architecture design, GoMatch
improves over prior geometric-based matching work with a reduction of
(10.67m,95.7deg) and (1.43m, 34.7deg) in average median pose errors on
Cambridge Landmarks and 7-Scenes, while requiring as little as 1.5/1.7% of
storage capacity in comparison to the best visual-based matching methods. This
confirms its potential and feasibility for real-world localization and opens
the door to future efforts in advancing city-scale visual localization methods
that do not require storing visual descriptors.

本文提出 GoMatch 作为大规模本地化中仅基于几何信息匹配图像关键点到地图的替代方案，使用 3D 点的旋转矢量表示地图，减少了存储需求并提高了实时性。实验表明，与最佳视觉匹配方法相比，GoMatch 在 Cambridge Landmarks 和 7-Scenes 上的平均姿态中位误差分别降低了 (10.67 米，95.7 度) 和 (1.43 米，34.7 度)，并且只需最佳视觉匹配方法存储能力的 1.5/1.7%。