  Depth completion aims at predicting dense pixel-wise depth from an extremely
sparse map captured from a depth sensor, e.g., LiDARs. It plays an essential
role in various applications such as autonomous driving, 3D reconstruction,
augmented reality, and robot navigation. Recent successes on the task have been
demonstrated and dominated by deep learning based solutions. In this article,
for the first time, we provide a comprehensive literature review that helps
readers better grasp the research trends and clearly understand the current
advances. We investigate the related studies from the design aspects of network
architectures, loss functions, benchmark datasets, and learning strategies with
a proposal of a novel taxonomy that categorizes existing methods. Besides, we
present a quantitative comparison of model performance on three widely used
benchmarks, including indoor and outdoor datasets. Finally, we discuss the
challenges of prior works and provide readers with some insights for future
research directions.
