  In this work we consider UAVs as cooperative agents supporting human users in
their operations. In this context, the 3D localisation of the UAV assistant is
an important task that can facilitate the exchange of spatial information
between the user and the UAV. To address this in a data-driven manner, we
design a data synthesis pipeline to create a realistic multimodal dataset that
includes both the exocentric user view, and the egocentric UAV view. We then
exploit the joint availability of photorealistic and synthesized inputs to
train a single-shot monocular pose estimation model. During training we
leverage differentiable rendering to supplement a state-of-the-art direct
regression objective with a novel smooth silhouette loss. Our results
demonstrate its qualitative and quantitative performance gains over traditional
silhouette objectives. Our data and code are available at
https://vcl3d.github.io/DronePose
