SeqLoc: Beyond the Single Frame for Cross-View Geo-Localization in Feature-Sparse Scenes

• URL: http://arxiv.org/abs/2608.07835v2
• Date: Tue, 11 Aug 2026 21:29:07 GMT
• Title: SeqLoc: Beyond the Single Frame for Cross-View Geo-Localization in Feature-Sparse Scenes
• Abstract summary: Cross-View Geo-Localization with OpenStreetMap (OSM) performs well in structure-rich urban environments but collapses in feature-sparse scenes such as rural roads.<n>We introduce CV-FSS, a benchmark that pairs sequential panoramas from five rural regions with aligned OSM maps, on which single-frame methods degrade drastically.<n>We then propose SeqLoc, an online test-time sequence aggregation mechanism that maintains a log-belief volume with three key components.
• Score: 49.30971835891526
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cross-View Geo-Localization (CVGL) with OpenStreetMap (OSM) performs well in structure-rich urban environments but collapses in feature-sparse scenes such as rural roads. To study this failure mode, in this work, we introduce CV-FSS, a benchmark that pairs sequential panoramas from five rural regions with aligned OSM maps, on which single-frame methods degrade drastically. We then propose SeqLoc, an online test-time sequence aggregation mechanism that recursively maintains a log-belief volume with three key components: (1) Entropy-Tempered Uncertainty (ETU) tempers each incoming pose likelihood volume by its normalized entropy; (2) Map-Guided Relocalization (MGR) mixes a map-shaped recovery distribution into the belief so that a suppressed true pose can recover; (3) Peak-Anchored Smoothing (PAS) derives the final pose at sub-grid precision. Extensive experiments on CV-FSS and CV-RHO demonstrate that SeqLoc outperforms single-frame localization by a large margin, improving both position and orientation recall by over 50%. The benchmark and source code are publicly available at https://zhengjunwei.com/publications/SeqLoc/SeqLoc.html.

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