Survey on Monocular Depth Estimation for Unmanned Aerial Vehicles using Deep Learning

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, highlighting the challenges that remain. To the best of our knowledge, this is the first survey covering this field.