Cross-view image matching for geo-localisation is a challenging problem due
to the significant visual difference between aerial and ground-level
viewpoints. The method provides localisation capabilities from geo-referenced
images, eliminating the need for external devices or costly equipment. This
enhances the capacity of agents to autonomously determine their position,
navigate, and operate effectively in environments where GPS signals are
unavailable. Current research employs a variety of techniques to reduce the
domain gap such as applying polar transforms to aerial images or synthesising
between perspectives. However, these approaches generally rely on having a
360{\deg} field of view, limiting real-world feasibility. We propose BEV-CV, an
approach which introduces two key novelties. Firstly we bring ground-level
images into a semantic Birds-Eye-View before matching embeddings, allowing for
direct comparison with aerial segmentation representations. Secondly, we
introduce the use of a Normalised Temperature-scaled Cross Entropy Loss to the
sub-field, achieving faster convergence than with the standard triplet loss.
BEV-CV achieves state-of-the-art recall accuracies, improving feature
extraction Top-1 rates by more than 300%, and Top-1% rates by approximately
150% for 70{\deg} crops, and for orientation-aware application we achieve a 35%
Top-1 accuracy increase with 70{\deg} crops.

我们提出了 BEV-CV 方法，该方法通过将地面图像转化为语义鸟瞰图进行嵌入匹配，并引入了一种归一化温度缩放的交叉熵损失函数，相比标准三元损失函数具有更快的收敛速度。BEV-CV 在 70 度裁剪图上实现了最新的召回率准确性，其特征提取 Top-1 率提高了 300% 以上，Top-1% 率提高了约 150%，对于方向感知应用，我们在 70 度裁剪图上实现了 35% 的 Top-1 准确率提高。