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.
3D建筑重建是从成像数据中提取3D模型的重要任务，涵盖了从城市规划到侦察等多种应用。现代的新视角合成（NVS）方法，如NeRF和高斯斑点化，提供了强大的技术，可以无监督地从自然的2D图像中开发3D模型。这些算法通常需要围绕感兴趣场景的输入训练视角，然而对于大型建筑物来说，通常不可能在所有摄像机高度都获得完整的训练视角。特别是，大多数建筑物最容易获得的摄像机视角包括接近地面（例如使用手机）和空中（无人机）高度。然而，由于无人机和地面图像集之间视角显著不同，新视角合成算法所需的摄像机注册步骤通常会失败。 在这项工作中，我们提出了一种名为DRAGON的方法，它可以接受无人机和地面建筑图像作为输入，并生成3D的新视角合成模型。DRAGON的关键洞察是，中间高度图像可以通过带有感知正则化的迭代过程由NVS算法自身进行外推，从而弥合两种高度之间的视觉特征差距，并实现注册。我们使用Google Earth Studio编制了一个半合成数据集，包括9个大型建筑场景，通过定量和定性方法展示，DRAGON相较于基准策略在该数据集上能够生成引人入胜的渲染效果。