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

Is Geometry Enough for Matching in Visual Localization?