AerialMetric: Benchmarking and Adapting UAV Monocular Metric Depth Estimation in the Real World

2026-06-28

AI Summary

This study addresses the performance degradation of metric depth estimation from monocular aerial images caused by domain shift. To this end, the authors introduce AerialMetric, the first multi-source benchmark dataset tailored to real-world drone perspectives, comprising 52K real and 16K synthetic image-depth pairs, with ground-truth depths accurately derived via photogrammetry. Leveraging this benchmark, they systematically evaluate the generalization capabilities of existing methods in aerial scenarios and establish new state-of-the-art performance through fine-tuning. The proposed dataset and evaluation protocol comprehensively support cross-domain adaptation research, achieving the best-reported metric depth estimation accuracy across diverse aerial scenes.

Abstract

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/.

Problem

• monocular metric depth estimation
• UAV aerial imagery
• domain gap
• aerial viewpoint
• depth prediction

Innovation

• monocular metric depth estimation
• UAV aerial imagery
• domain adaptation
• benchmark dataset
• photogrammetry