Loc$^2$: Interpretable Cross-View Localization via Depth-Lifted Local Feature Matching

• URL: http://arxiv.org/abs/2509.09792v2
• Date: Mon, 29 Sep 2025 14:04:01 GMT
• Title: Loc$^2$: Interpretable Cross-View Localization via Depth-Lifted Local Feature Matching
• Authors: Zimin Xia, Chenghao Xu, Alexandre Alahi,
• Abstract summary: We propose an accurate and interpretable fine-grained cross-view localization method.<n>It estimates the 3 Degrees of Freedom (DoF) pose of a ground-level image by matching its local features with a reference aerial image.<n> Experiments show state-of-the-art accuracy in challenging scenarios such as cross-area testing and unknown orientation.
• Score: 80.57282092735991
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose an accurate and interpretable fine-grained cross-view localization method that estimates the 3 Degrees of Freedom (DoF) pose of a ground-level image by matching its local features with a reference aerial image. Unlike prior approaches that rely on global descriptors or bird's-eye-view (BEV) transformations, our method directly learns ground-aerial image-plane correspondences using weak supervision from camera poses. The matched ground points are lifted into BEV space with monocular depth predictions, and scale-aware Procrustes alignment is then applied to estimate camera rotation, translation, and optionally the scale between relative depth and the aerial metric space. This formulation is lightweight, end-to-end trainable, and requires no pixel-level annotations. Experiments show state-of-the-art accuracy in challenging scenarios such as cross-area testing and unknown orientation. Furthermore, our method offers strong interpretability: correspondence quality directly reflects localization accuracy and enables outlier rejection via RANSAC, while overlaying the re-scaled ground layout on the aerial image provides an intuitive visual cue of localization accuracy.

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