GACLoc: Ground-Anchor Geometry Modeling for Cross-View Localization

Cross-view geo-localization estimates the planar camera pose of a ground image with respect to geo-referenced satellite imagery. Recent correspondence-based cross-view localization methods enable interpretable camera pose recovery through explicit ground–satellite matches. However, learning such correspondences remains challenging because training data usually provide only camera-pose supervision, without pixel-level ground–satellite correspondence annotations. Cross-view correspondence further exhibits an inherent geometric asymmetry: under a known ground sampling distance (GSD), equal-sized satellite tokens correspond to approximately fixed metric regions, whereas perspective ground tokens correspond to depth-dependent regions whose size, shape, and orientation vary substantially. Moreover, in repetitive urban scenes, visual similarity alone is insufficient to establish reliable correspondences, as incorrect satellite locations can receive high matching scores. To address these issues, we propose GACLoc: Ground-Anchor Geometry Modeling for Cross-View Localization (GACLoc), a geometry-aware correspondence framework with two key components: Anchor Geometry Representation (AGR) and Geometry-Aware Local Correspondence Refinement (GALCR). AGR models the metric region geometry of ground anchors and uses it to construct geometry-based candidates and geometric features, while GALCR fuses visual and geometric features to obtain more reliable soft correspondences for weighted analytic pose recovery. Experiments demonstrate competitive localization accuracy under challenging settings, including cross-area evaluation and unknown orientation.