AerialMetric: Benchmarking and Adapting UAV Monocular Metric Depth Estimation in the Real World
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...
| Authors | Zhongqiang Song, Guanying Chen, Yuqi Zhang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 29 June 2026 |
| Last updated at source | 29 July 2026 |
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- Kuiee/AerialMetric-ECCV2026Dataset · cited by
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- Kuiee/AerialMetric-ECCV2026Dataset
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