发表机构
Aerospace Information Research Institute, Chinese Academy of Sciences; School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences; University of Chinese Academy of Sciences; Key Laboratory of Target Cognition and Application Technology (TCAT), Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院; 中国科学院大学电子电气与通信工程学院; 中国科学院大学; 中国科学院空天信息创新研究院目标认知与应用技术重点实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对无人机单目深度估计在多样视角和大尺度深度分布下的难题，提出DAPM模型，通过建立视角定量表示，引入IGD和PQB模块，在UAPD数据集上实验，该模型在深度和相机姿态估计方面达最优性能。
AI 中文摘要
单目深度估计是无人机三维重建和自主导航的基本前提。在实际部署中，无人机在高度、俯仰、横滚和视场角不断变化的高度动态相机姿态下运行。现有方法难以应对多样视角和航拍场景中深度分布的大尺度变化。本文通过理论分析建立无人机视角的定量表示，提出任意视角深度估计模型（DAPM），这是首个专为无人机航拍图像设计的单目框架，可在连续变化视角下联合估计相机姿态和深度。具体介绍了理想地面深度（IGD）模块和粗到精的渐进量化仓（PQB）模块。通过引入渐进监督和分层量化仓，PQB模块能在复杂无人机航拍图像中实现稳健估计。为评估该框架，提出了无人机任意视角深度（UAPD）数据集。实验结果表明DAPM在深度和相机姿态估计指标上均达到了当前最优性能。
英文摘要
Monocular depth estimation is a fundamental prerequisite for 3D reconstruction and autonomous navigation in Unmanned Aerial Vehicles (UAVs). In practical deployments, UAVs operate under highly dynamic camera poses characterized by continuous variations in height, pitch, roll, and field of view (FOV). Existing monocular depth estimation methods frequently fail to generalize across such diverse perspectives and the expansive scale of depth distributions inherent in aerial scenes. To address these challenges, we establish a quantitative representation of UAV viewing angles through rigorous theoretical analysis, deriving the geometric correspondence between viewing angles and view distances using the ground plane as a reference for observation. Building upon this, we propose Depth Estimation for Any Perspectives Model (DAPM), representing the first monocular framework specifically designed for UAV aerial imagery to jointly estimate camera pose and depth under continuously varying viewpoints. Specifically, we introduce an Ideal Ground Depth (IGD) module that leverages the derived geometric relationships between UAV perspectives and view distances to implement dense camera-pose supervision and enhance depth features. And we further develop a coarse-to-fine Progressive Quantization Bins (PQB) module. By incorporating progressive supervision and hierarchical quantization bins, the PQB module enables robust estimation in complex UAV aerial imagery. To evaluate the proposed framework, we present the UAV Any Perspectives Depth (UAPD) dataset, featuring comprehensive and continuous distributions of pose parameters. Experimental results on UAPD demonstrate that DAPM achieves state-of-the-art performance across both depth and camera-pose estimation metrics. The source code and datasets are available at: https://github.com/ThisIsLT/DAPM.
