DAPM: UAV Monocular Depth Estimation from Any Height, Pitch, Roll and FOV
Monocular depth estimation is a fundamental prerequisite for 3D reconstruction and autonomous navigation in Unmanned Aerial Vehicles (UAVs). In practical deployments, UAVs operate under highly dynamic camera poses characterized by continuous variations in height, pitch, roll, and field of view (FOV). Existing monocular depth estimation methods frequently fail to generalize across such diverse perspectives and the expansive scale of depth distributions inherent in aerial scenes. To address these challenges, we establish a quantitative representation of UAV viewing angles through rigorous theore
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- FuzzyOverlapping authors or contributors · 62%google-research/google-research →
“Shared author/contributor keys: sun”
- LinkedLinked via arxiv author · 85%Xitong Ling →
“DAPM: UAV Monocular Depth Estimation from Any Height, Pitch, Roll and FOV”
- LinkedLinked via arxiv author · 85%Wenhui Diao →
“DAPM: UAV Monocular Depth Estimation from Any Height, Pitch, Roll and FOV”
- LinkedLinked via arxiv author · 85%Yingchao Feng →
“DAPM: UAV Monocular Depth Estimation from Any Height, Pitch, Roll and FOV”
- LinkedLinked via arxiv author · 85%Hanbo Bi →
“DAPM: UAV Monocular Depth Estimation from Any Height, Pitch, Roll and FOV”
- LinkedLinked via arxiv author · 85%Zhongyan Hou →
“DAPM: UAV Monocular Depth Estimation from Any Height, Pitch, Roll and FOV”
- LinkedLinked via arxiv author · 85%Xian Sun →
“DAPM: UAV Monocular Depth Estimation from Any Height, Pitch, Roll and FOV”