  3D building reconstruction from imaging data is an important task for many
applications ranging from urban planning to reconnaissance. Modern Novel View
synthesis (NVS) methods like NeRF and Gaussian Splatting offer powerful
techniques for developing 3D models from natural 2D imagery in an unsupervised
fashion. These algorithms generally require input training views surrounding
the scene of interest, which, in the case of large buildings, is typically not
available across all camera elevations. In particular, the most readily
available camera viewpoints at scale across most buildings are at near-ground
(e.g., with mobile phones) and aerial (drones) elevations. However, due to the
significant difference in viewpoint between drone and ground image sets, camera
registration - a necessary step for NVS algorithms - fails. In this work we
propose a method, DRAGON, that can take drone and ground building imagery as
input and produce a 3D NVS model. The key insight of DRAGON is that
intermediate elevation imagery may be extrapolated by an NVS algorithm itself
in an iterative procedure with perceptual regularization, thereby bridging the
visual feature gap between the two elevations and enabling registration. We
compiled a semi-synthetic dataset of 9 large building scenes using Google Earth
Studio, and quantitatively and qualitatively demonstrate that DRAGON can
generate compelling renderings on this dataset compared to baseline strategies.
