AerialMetric: 在真实世界中基准测试和适配无人机单目度量深度估计
AerialMetric: Benchmarking and Adapting UAV Monocular Metric Depth Estimation in the Real World
AI总结：
针对无人机航拍图像中的单目度量深度估计问题，构建了包含5.2万真实和1.6万合成图像-深度对的基准数据集，系统评估现有模型并微调代表性方法，实现航拍场景下的最优性能。
摘要：
本文研究了无人机航拍图像中的单目度量深度估计问题。尽管近期基于数据驱动的方法在地面场景中取得了显著进展，但主要基于街景和室内数据集训练的模型在应用于航拍视角时存在显著的领域差距。为应对这些挑战，我们引入了AerialMetric，一个旨在评估和促进无人机航拍视角下单目度量深度估计适配的基准数据集。该数据集包含从不同来源收集的四个互补子集，共同覆盖真实世界摄影测量数据、受控航拍采集场景、逼真合成场景以及野外互联网图像。总计，AerialMetric提供了5.2万对真实和1.6万对合成图像-深度对，并带有可靠的度量真值。基于该数据集，我们对现有最先进模型在航拍场景下进行了系统评估，并研究了视角、高度和相机参数对度量深度预测的影响。此外，通过在我们的数据集上微调代表性度量深度模型，我们建立了一个全面的航拍基准，并在多样化的航拍图像上实现了最先进的性能。我们的数据集、代码和模型权重已在https://kuieless.github.io/AerialMetric-ECCV2026-page/ 公开。
英文摘要：
This paper addresses the problem of monocular metric depth estimation in aerial UAV imagery. Although recent data-driven methods have achieved remarkable progress in ground-level scenarios, models trained primarily on street-view and indoor datasets exhibit significant domain gaps when applied to aerial viewpoints. To tackle these challenges, we introduce AerialMetric, a benchmark dataset designed to evaluate and facilitate the adaptation of monocular metric depth estimation under UAV aerial viewpoints. The dataset consists of four complementary subsets collected from different sources, jointly covering real-world photogrammetry data, controlled aerial acquisition settings, photorealistic synthetic scenes, and in-the-wild Internet imagery. Totally, AerialMetric provides 52K real-world and 16K synthetic image-depth pairs with reliable metric ground truth. Based on this dataset, we conduct systematic evaluations of existing state-of-the-art models under aerial settings and investigate the impact of viewpoint, altitude, and camera parameters on metric depth prediction. In addition, by fine-tuning representative metric depth model on our dataset, we establish a comprehensive aerial benchmark and achieve state-of-the-art performance across diverse aerial imagery. Our dataset, code, and model weight are publicly available at https://kuieless.github.io/AerialMetric-ECCV2026-page/.
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。
