RHO: Robust Holistic OSM-Based Metric Cross-View Geo-Localization
Junwei Zheng, Ruize Dai, Ruiping Liu, Zichao Zeng, Yufan Chen, Fangjinhua Wang, Kunyu Peng, Kailun Yang
Abstract
Metric Cross-View Geo-Localization (MCVGL) aims to estimate the 3-DoF camera pose (position and heading) by matching ground and satellite images. In this work, instead of pinhole and satellite images, we study robust MCVGL using holistic panoramas and OpenStreetMap (OSM). To this end, we establish a large-scale MCVGL benchmark dataset, CV-RHO, with over 2.7M images under different weather and lighting conditions, as well as sensor noise. Furthermore, we propose a model termed RHO with a two-branch Pin-Pan architecture for accurate visual localization. A Split-Undistort-Merge (SUM) module is introduced to address the panoramic distortion, and a Position-Orientation Fusion (POF) mechanism is designed to enhance the localization accuracy. Extensive experiments prove the value of our CV-RHO dataset and the effectiveness of the RHO model, with a significant performance gain up to 20% compared with the state-of-the-art baselines. Project page: https://github.com/InSAI-Lab/RHO.
BibTeX
@inproceedings{cvpr2026_rhorobustholisti,
title = {RHO: Robust Holistic OSM-Based Metric Cross-View Geo-Localization},
author = {Junwei Zheng and Ruize Dai and Ruiping Liu and Zichao Zeng and Yufan Chen and Fangjinhua Wang and Kunyu Peng and Kailun Yang and Jiaming Zhang and Rainer Stiefelhagen},
booktitle = {CVPR 2026},
year = {2026}
}