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• W4322731652 abstract "Deep learning-based solutions for the ill-posed problem of Monocular Depth Estimation (MDE) from 2D color images have shown potential in recent years, spurring a very active field of research. Most state-of-the-art proposals focus on solving the problem in the context of automotive advanced driver assistance and/or autonomous driving systems. While presenting their own complexities and challenges, the vast majority of road environments exhibit a number of commonalities amongst themselves. The aerial domain in which modern Unmanned Aerial Vehicles (UAVs) operate is significantly different and features a large variety of possible scenes based on the specific mission carried out. The increasing number of applications for UAVs could benefit from more advanced learning-based MDE solutions for recovering 3D geometric information from the scene. In this paper, we conduct a study of existing research on the topic of MDE specifically tailored for aerial views, as well as presenting the datasets and tools currently supporting such research, high-lighting the challenges that remain. To the best of our knowledge, this is the first survey covering this field." @default.
• W4322731652 created "2023-03-03" @default.
• W4322731652 creator A5010543525 @default.
• W4322731652 creator A5047954457 @default.
• W4322731652 date "2022-09-22" @default.
• W4322731652 modified "2023-09-28" @default.
• W4322731652 title "Survey on Monocular Depth Estimation for Unmanned Aerial Vehicles using Deep Learning" @default.
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• W4322731652 doi "https://doi.org/10.1109/iccp56966.2022.10053950" @default.
• W4322731652 hasPublicationYear "2022" @default.
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