  Estimating depth from single RGB images and videos is of widespread interest
due to its applications in many areas, including autonomous driving, 3D
reconstruction, digital entertainment, and robotics. More than 500 deep
learning-based papers have been published in the past 10 years, which indicates
the growing interest in the task. This paper presents a comprehensive survey of
the existing deep learning-based methods, the challenges they address, and how
they have evolved in their architecture and supervision methods. It provides a
taxonomy for classifying the current work based on their input and output
modalities, network architectures, and learning methods. It also discusses the
major milestones in the history of monocular depth estimation, and different
pipelines, datasets, and evaluation metrics used in existing methods.
