  Estimating depth from RGB images can facilitate many computer vision tasks,
such as indoor localization, height estimation, and simultaneous localization
and mapping (SLAM). Recently, monocular depth estimation has obtained great
progress owing to the rapid development of deep learning techniques. They
surpass traditional machine learning-based methods by a large margin in terms
of accuracy and speed. Despite the rapid progress in this topic, there are
lacking of a comprehensive review, which is needed to summarize the current
progress and provide the future directions. In this survey, we first introduce
the datasets for depth estimation, and then give a comprehensive introduction
of the methods from three perspectives: supervised learning-based methods,
unsupervised learning-based methods, and sparse samples guidance-based methods.
In addition, downstream applications that benefit from the progress have also
been illustrated. Finally, we point out the future directions and conclude the
paper.
