Accurate 3-DoF Camera Geo-Localization via Ground-to-Satellite Image Matching

TL;DR

This paper introduces a geometry-aware method for ground-to-satellite image geo-localization that achieves fine-grained camera positioning by combining improved retrieval with pixel-level localization, outperforming existing approaches.

Contribution

The paper presents a novel geometry-aware geo-localization framework that enhances coarse localization accuracy and achieves pixel-level precision in ground-to-satellite image matching.

Findings

Improved coarse localization accuracy over state-of-the-art methods.

Achieved pixel-level localization precision within satellite image resolution.

Demonstrated effectiveness through extensive experiments.

Abstract

We address the problem of ground-to-satellite image geo-localization, that is, estimating the camera latitude, longitude and orientation (azimuth angle) by matching a query image captured at the ground level against a large-scale database with geotagged satellite images. Our prior arts treat the above task as pure image retrieval by selecting the most similar satellite reference image matching the ground-level query image. However, such an approach often produces coarse location estimates because the geotag of the retrieved satellite image only corresponds to the image center while the ground camera can be located at any point within the image. To further consolidate our prior research findings, we present a novel geometry-aware geo-localization method. Our new method is able to achieve the fine-grained location of a query image, up to pixel size precision of the satellite image, once its coarse location and orientation have been determined. Moreover, we propose a new geometry-aware image retrieval pipeline to improve the coarse localization accuracy. Apart from a polar transform in our conference work, this new pipeline also maps satellite image pixels to the ground-level plane in the ground-view via a geometry-constrained projective transform to emphasize informative regions, such as road structures, for cross-view geo-localization. Extensive quantitative and qualitative experiments demonstrate the effectiveness of our newly proposed framework. We also significantly improve the performance of coarse localization results compared to the state-of-the-art in terms of location recalls.