We study an important, yet largely unexplored problem of large-scale cross-modal visual
localization by matching ground RGB images to a geo-referenced aerial LIDAR 3D point
cloud (rendered as depth images). Prior works were demonstrated on small datasets and
did not lend themselves to scaling up for large-scale applications. To enable large-scale
evaluation, we introduce a new dataset containing over 550K pairs (covering 143 km2 area)
of RGB and aerial LIDAR depth images. We propose a novel joint embedding based method …

We study an important, yet largely unexplored problem of large-scale cross-modal visual
localization by matching ground RGB images to a geo-referenced aerial LIDAR 3D point
cloud (rendered as depth images). Prior works were demonstrated on small datasets and
did not lend themselves to scaling up for large-scale applications. To enable large-scale
evaluation, we introduce a new dataset containing over 550K pairs (covering 143 km^ 2
area) of RGB and aerial LIDAR depth images. We propose a novel joint embedding based …