  Estimating depth from RGB images is a long-standing ill-posed problem, which
has been explored for decades by the computer vision, graphics, and machine
learning communities. In this article, we provide a comprehensive survey of the
recent developments in this field. We will focus on the works which use deep
learning techniques to estimate depth from one or multiple images. Deep
learning, coupled with the availability of large training datasets, have
revolutionized the way the depth reconstruction problem is being approached by
the research community. In this article, we survey more than 100 key
contributions that appeared in the past five years, summarize the most commonly
used pipelines, and discuss their benefits and limitations. In retrospect of
what has been achieved so far, we also conjecture what the future may hold for
learning-based depth reconstruction research.
