Modern cameras are equipped with a wide array of sensors that enable recording the geospatial context of an image. Taking advantage of this, we explore depth estimation under the assumption that the camera is geocalibrated, a problem we refer to as geo-enabled depth estimation. Our key insight is that if capture location is known, the corresponding overhead viewpoint offers a valuable resource for understanding the scale of the scene. We propose an end-to-end architecture for depth estimation that uses geospatial context to infer a synthetic ground-level depth map from a co-located overhead image, then fuses it inside of an encoder/decoder style segmentation network. To support evaluation of our methods, we extend a recently released dataset with overhead imagery and corresponding height maps. Results demonstrate that integrating geospatial context significantly reduces error compared to baselines, both at close ranges and when evaluating at much larger distances than existing benchmarks consider.

利用现代相机所配备的各种传感器记录图像的地理空间环境，将在已知摄像机拍摄地点的情况下，提出一种基于地理启用的方法，使用一个识别出的综合地面深度地图与相对应的俯瞰图像，将其融合在编码器/解码器风格的分割网络内，实现对深度估计的目的。结果表明，将地理上下文集成到深度估计模型中可显着降低误差。

利用地理空间上下文增强深度估计