TanDepth：利用全球数字高程模型实现无人机公制单目深度估计
TanDepth: Leveraging Global DEMs for Metric Monocular Depth Estimation in UAVs
AI总结：
TanDepth利用全球数字高程模型投影与布料模拟滤波器适配，在推理时从相对深度估计中恢复公制深度，适用于无人机，并扩展了UAVid数据集。
摘要：
空中场景理解系统面临严格的载荷限制，通常必须依赖单目深度估计来建模场景几何，这本质上是一个不适定问题。此外，获取基于学习方法所需的准确地面真值数据在空中领域带来了额外的重大挑战。自监督方法可以绕过这一问题，但代价是仅提供尺度模糊的结果。同样，近期在零样本泛化方面取得良好进展的监督解决方案也仅提供相对深度值。本文提出TanDepth，一种实用的尺度恢复方法，可在推理时从相对估计中获得公制深度结果，且不依赖于生成这些估计的模型类型。针对无人机（UAV）应用定制，我们的方法利用全球数字高程模型（GDEM）的稀疏测量，通过外参和内参信息将其投影到相机视图。我们提出对布料模拟滤波器的适配，该适配允许从估计的深度图中选择地面点，然后与投影的参考点进行关联。我们在多种真实场景中，将我们的方法与为无人机适配的替代缩放方法进行评估和比较。考虑到该领域数据的有限可用性，我们构建并发布了流行的UAVid数据集的一个全面的、以深度为重点的扩展，以促进进一步研究。
英文摘要：
Aerial scene understanding systems face stringent payload restrictions and must often rely on monocular depth estimation for modeling 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 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
