Monocular depth estimation is a classical computer vision task, which consists in densely predicting the spatial distance between the object depicted by each pixel and the camera with which the single RGB image is taken. This type of information is extremely useful for a variety of practical contexts, like 3D reconstruction, visual simultaneous localization and mapping (SLAM), and autonomous driving systems, because it permits reasoning about the geometrical structure of the environment and the relationship between objects in it. Over the last years, a number of fully-convolutional encoder-decoder networks have been used to study the considered problem; their popularity is rooted in their locality and translation invariance properties, which allow a parameter-efficient modelling of highly spatially-correlated information.