Cross-view Video Geo-localization (CVG) aims to localize ground-view videos by retrieving their corresponding geo-tagged aerial images. However, CVG approaches rely on fixed-length inputs and post-hoc refinement, hindering online-oriented localization under partial or dynamic observations. In this work, we formulate Progressive Cross-view Video Geo-localization (PCVG) as a deployment-oriented extension and evaluation protocol of CVG, enabling localization under varying temporal budgets, prefix-based inference, random-start evaluation, and long-range localization with interruptions. To explore PCVG, we introduce X$^2$Localizer, a cross-grained alignment framework that jointly supervises global prefix-to-aerial retrieval and token-aggregated frame--aerial-tile matching with a budget-dependent asymmetric objective. Furthermore, we introduce a Sliding-Window Re-Localization (SWRL) strategy that dynamically refreshes candidate regions for failure recovery and long-range deployment without full-sequence reprocessing. Extensive experiments show that X$^2$Localizer preserves conventional full-video performance, with marginal gains of +0.1 Recall@1 and +0.3 Recall@10, while substantially improving early localization. In the challenging single-frame setting, X$^2$Localizer improves coarse retrieval by +4.7 Recall@1 and +11.5 Recall@10 over the previous state-of-the-art method. With SWRL, our approach further enables robust progressive localization under random-start and long-distance scenarios, narrowing the gap between benchmark evaluation and real-world deployment.
Zichao Zeng, Weijia Fan, Yufan Chen et al.· 0 citations
Abstract. Estimating camera poses jointly from aerial and ground imagery remains difficult because large viewpoint changes reduce overlap, alter appearance, and weaken the geometric assumptions relied on by both classical photogrammetry and recent AI-based reconstruction models. This paper presents a controlled comparison between a classic photogrammetric approach represented by COLMAP and a cross-view fine-tuned end-to-end model based on Dust3R. Tests are carried out on a London building scene containing 10 aerial and 29 ground images. Fine-tuned Dust3R reconstructs the full image set, whereas COLMAP successfully registers 24 ground-level images. Because both reconstructions are defined only up to an unknown similarity transform and no ground-truth poses are available, we evaluate the shared subset through 7-DoF similarity transformation analysis rather than direct metric pose errors. After transformation, the translation RMSE of the shared camera centres is 10.0% of the reconstructed scene diagonal in the fine-tuned Dust3R coordinate frame. We further compare pairwise geometric support using a unified fundamental-matrix RANSAC evaluation over 406 image pairs. The AI-based pipeline achieves substantially higher inlier ratios than photogrammetric pipeline under the same verification settings, indicating more successful cross-view orientation. The study contributes a clearer evaluation protocol for mixed aerial-ground pose estimation without ground truth, together with an empirical analysis of robustness, alignment behaviour, and current limitations of both pipelines.
Zichao Zeng, June Moh Goo, Jan Boehm· The International Archives o...· 1 citation
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