Estimating depth information from images is a fundamental and critical job in computer vision, as it may be utilized in a large range of applications such as simultaneous localization and mapping, navigation, object identification, and semantic segmentation. Depth extraction can be faced with different techniques: geometry-based (stereo-matching, structure from motion), sensor-based (LiDAR, structured-light, TOF), and deep learning-based. In particular, monocular depth estimation is the challenge of predicting a depth map using just a single RGB image as input. This significantly reduces the cost and the power consumption for robotics and embedded devices. However, it is frequently described as an ill-posed problem, since an infinite number of 3D scenes might actually correspond to a single 2D view of a scene.