Aerial scene understanding systems face stringent payload restrictions and must often rely on monocular depth estimation for modelling scene geometry, which is an inherently ill-posed problem. Moreover, obtaining accurate ground truth data required by learning-based methods raises significant additional challenges in the aerial domain. Self-supervised approaches can bypass this problem, at the cost of providing only up-to-scale results. Similarly, recent supervised solutions which make good progress towards zero-shot generalization also provide only relative depth values. This work presents TanDepth, a practical, online scale recovery method for obtaining metric depth results from relative estimations at inference-time, irrespective of the type of model generating them. Tailored for Unmanned Aerial Vehicle (UAV) applications, our method leverages sparse measurements from Global Digital Elevation Models (GDEM) by projecting them to the camera view using extrinsic and intrinsic information. An adaptation to the Cloth Simulation Filter is presented, which allows selecting ground points from the estimated depth map to then correlate with the projected reference points. We evaluate and compare our method against alternate scaling methods adapted for UAVs, on a variety of real-world scenes. Considering the limited availability of data for this domain, we construct and release a comprehensive, depth-focused extension to the popular UAVid dataset to further research.

本研究针对无人机领域中单目深度估计的挑战，提出了一种新的在线尺度恢复方法TanDepth，以获得度量深度结果。通过利用全球数字高程模型的稀疏测量，该方法能够在推理时处理相对估计结果，进而解决相对深度值与实际尺度之间的转换问题。研究表明，该方法在多种真实场景中表现优于现有的尺度调整方法，具有潜在的广泛应用价值。

TanDepth：利用全球数字高程模型进行无人机的度量单目深度估计