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.

提出了 DRAGON 方法，能够将无人机和地面建筑图像作为输入，并生成 3D Novel View synthesis 模型，通过感知正则化的迭代过程填补了两种高度之间的视觉特征差距，从而实现了相机配准。在使用 Google Earth Studio 编译的 9 个大型建筑场景的半合成数据集上，定量和定性地证明了与基准策略相比，DRAGON 能够生成令人信服的渲染效果。