Orientation-Guided Homography for Fine-Grained Cross-View Localization.

Cross-view localization aims to estimate the precise position and orientation of a ground-view image by aligning it with satellite imagery. However, existing homography-based methods are typically evaluated under limited orientation noise (±45°) and exhibit limited refinement capability, as their local correlation-based refinement relies on a reasonably good initial orientation estimate. To address these limitations, we propose a fine-grained cross-view localization method based on orientation-guided homography (OGH-Net), whose core idea is to predict an initial orientation prior that explicitly guides subsequent homography refinement. Specifically, we first design a hybrid bird's-eye-view (BEV) transformation to generate BEV images with preserved central geometry and expanded coverage. Then, we introduce a lightweight orientation-prior module that provides a coarse yaw estimate across the full ±180° range. Finally, we develop a multiscale iterative homography module that progressively refines the projection matrix through hierarchical iterations across multiple feature resolutions. Under cross-area, unknown-orientation conditions, it reduces mean localization error by 11% and mean orientation error by 27% on VIGOR, and further reduces mean localization error by 27% on KITTI compared with previous state-of-the-art methods. Moreover, OGH-Net runs in real time at up to 107 FPS on a single RTX 3090 GPU, offering a favorable trade-off between accuracy and efficiency. The code and trained models will be released at https://github.com/YC-Zhang2025/OGH-Net.