Abstract
Cross-view Geo-localisation is typically performed at a coarse granularity, because densely sampled satellite image patches overlap heavily. This heavy overlap would make dis-ambiguating patches very challenging. However, by opting for sparsely sampled patches, prior work has placed an artificial upper bound on the localisation accuracy that is possible. Even a perfect oracle system cannot achieve accuracy greater than the average separation of the tiles. To solve this limitation, we propose combining cross-view geo-localisation and relative pose estimation to increase precision to a level practical for real-world application. We develop PEnG, a 2-stage system which first predicts the most likely edges from a city-scale graph representation upon which a query image lies. It then performs relative pose estimation within these edges to determine a precise position. PEnG presents the first technique to utilise both viewpoints available within cross-view geo-localisation datasets, referring to this as Multi-View Geo-Localisation (MVGL). This enhances accuracy to a sub-metre level, with some examples achieving centimetre level precision. Our proposed ensemble achieves state-of-the-art accuracy-with relative Top-5m retrieval improvements on previous works of 213%. Decreasing the median Euclidean distance error by 96.90% from the previous best of 734m down to 22.77m, when evaluating with 90° horizontal FOV images. Code is available here: github.com/tavisshore/peng.