OriLoc: Unlimited-FoV and Orientation-Free Cross-View Geolocalization

Cross-view image-based geolocalization can provide accurate, drift-free navigation in the absence of external positioning signals, which is particularly crucial in domains such as UAV delivery logistics and disaster relief. While existing research predominantly focuses on matching ground panoramic images with known orientations, real-world scenarios often present images with unknown orientations. Additionally, acquiring panoramic images with a full <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$360^\circ$</tex-math></inline-formula> field of view (FoV) is impractical, creating a disconnect between research and practical applications. In this study, we introduce an innovative cross-view geolocalization method that integrates sophisticated orientation estimation. Our goal is to improve geolocalization accuracy in limited FoV and arbitrary orientation scenarios. To achieve this, we design an efficient hard sample training strategy employing a dual-weighted soft-margin triplet loss function to extract discriminative features. Concurrently, we develop an orientation estimation module that leverages a convolution-based sliding window to assess the similarity with satellite-view embeddings and query embeddings. Thus, our method is named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OriLoc</i>. We validated the approach's unique outperformance on three challenging cross-view geolocalization datasets, including data from commercial, residential, urban landscapes, and suburban areas across two continents, aligning with the demands of real-world scenarios. Our results demonstrate that a straightforward hard sample mining strategy, when combined with an appropriate learning objective, significantly enhances geolocalization performance for limited FoV and orientation-free cross-view images, as shown in Figure 1. Furthermore, our orientation estimation module, which incorporates classical convolutions, exhibits remarkable accuracy in estimating orientations when integrated with attention embeddings prior to polar transformation. Code and trained models are publicly available on <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/boni-hu/OriLoc</uri>.