论文标题
丹麦航空和地面：空中街道级别的识别和本地化的数据集
Danish Airs and Grounds: A Dataset for Aerial-to-Street-Level Place Recognition and Localization
论文作者
论文摘要
在宽基线配置中，位置识别和视觉定位尤其具有挑战性。在本文中，我们用\ emph {丹麦航空和地面}（DAG）数据集做出了贡献，该数据集是针对此类情况的大量街道和空中图像。它的主要挑战在于查询图像和参考图像之间的极端观看角差异，随之而来的是照明和透视图。该数据集比当前可公开可用的数据更大，更多样化，包括城市，郊区和农村地区的50公里道路。所有图像均与准确的6-DOF元数据相关联，该元数据允许进行视觉定位方法的基准测试。 我们还提出了一个地图到图像重新定位管道，该管道首先估算了从空中图像中进行密集的3D重建，然后将查询街道级图像与3D模型的街道效果匹配。数据集可以在以下位置下载：https：//frederikwarburg.github.io/dag
Place recognition and visual localization are particularly challenging in wide baseline configurations. In this paper, we contribute with the \emph{Danish Airs and Grounds} (DAG) dataset, a large collection of street-level and aerial images targeting such cases. Its main challenge lies in the extreme viewing-angle difference between query and reference images with consequent changes in illumination and perspective. The dataset is larger and more diverse than current publicly available data, including more than 50 km of road in urban, suburban and rural areas. All images are associated with accurate 6-DoF metadata that allows the benchmarking of visual localization methods. We also propose a map-to-image re-localization pipeline, that first estimates a dense 3D reconstruction from the aerial images and then matches query street-level images to street-level renderings of the 3D model. The dataset can be downloaded at: https://frederikwarburg.github.io/DAG
