ECCV 2026｜AerialMetric：无人机单目深度估计新突破

TL;DR - AerialMetric (ECCV 2026) is a benchmark and dataset for UAV monocular metric depth estimation, plus MoGe2-Aerial, a LoRA-adapted model that fixes the scale bias ground-trained depth models exhibit on drone imagery.

• Dataset: ~52K real and ~16K synthetic RGB-depth pairs across four complementary subsets — Oblique (25 real oblique-photogrammetry scenes, ~47K pairs), Decoupled (4 controlled scenes, ~4.6K pairs), Synthetic (7 scenes, ~16K pairs), and Wild (100 internet drone videos, ~1.1K pairs, eval-only).
• The Decoupled subset orthogonally varies 4 pitch angles × 2 altitudes × 2 FOVs so the usually-entangled effects of flight height, pitch, and field of view can be analyzed separately.
• Ground truth pipelines differ by source: LiDAR point-cloud projection or multi-view mesh rendering for real flights; Unreal Engine render buffers / Google Earth Studio reconstruction for synthetic; and pose + landmark-size-based global scaling to convert relative depth to pseudo-metric depth for internet video.
• Method: LoRA fine-tuning of MoGe2 on a mix of real aerial, synthetic, and a small amount of ground data yields MoGe2-Aerial, reported to improve AbsRel/δ1 on aerial, controlled, and in-the-wild sets while largely retaining ground-scene performance (paper reports tables; specific numbers not given in this excerpt).