AerialMetric: Benchmarking and Adapting UAV Monocular Metric Depth Estimation in the Real World
Abstract Overview
This paper studies monocular metric depth estimation for UAV aerial imagery, where models trained on ground-level data show substantial domain gaps. To support evaluation and adaptation, the authors introduce AerialMetric, a benchmark composed of four subsets: real-world oblique photogrammetry data, a controlled decoupled UAV test set, photorealistic synthetic data, and in-the-wild internet drone videos with pseudo-metric labels. The dataset contains 52K real-world and 16K synthetic image-depth pairs with metric or pseudo-metric supervision, and is designed to analyze the effects of viewpoint, altitude, and camera field of view. Using this benchmark, the paper evaluates several existing metric depth estimators and fine-tunes a representative model to assess how aerial-specific training changes performance.
Novelty
The main novelty is the construction of a UAV-focused metric depth benchmark that combines large-scale real and synthetic data with two distinctive evaluation settings: a variable-decoupled acquisition protocol and an in-the-wild pseudo-metric test set. This setup enables systematic analysis of aerial imaging factors that are usually entangled or missing in prior depth benchmarks.
Results
The experiments show that existing zero-shot metric depth models transfer poorly to aerial imagery, often with very high AbsRel and near-zero δ1 on AerialMetric. After fine-tuning MoGe2 on AerialMetric, performance improves substantially across aerial benchmarks; for example, on AerialMetric-Oblique-City without ground-truth intrinsics, δ1 rises from 5.1 to 89.3, and on AerialMetric-Wild (0–400 m), AbsRel drops from 55.26 to 21.34 while δ1 increases from 9.5 to 53.7. The adapted model also remains competitive on several ground-domain benchmarks, indicating limited loss of cross-domain generalization.
Key Points
- AerialMetric combines four complementary subsets to cover curated real UAV imagery, controlled decoupled captures, synthetic training data, and in-the-wild aerial videos.
- The benchmark exposes a severe domain gap between ground-trained monocular metric depth models and aerial UAV viewpoints, including sensitivity to altitude, pitch, and field of view.
- Fine-tuning a representative model on AerialMetric yields large gains on aerial depth estimation while preserving competitive performance on multiple ground-domain datasets.