  Monocular depth estimation (MDE) aims to transform an RGB image of a scene
into a pixelwise depth map from the same camera view. It is fundamentally
ill-posed due to missing information: any single image can have been taken from
many possible 3D scenes. Part of the MDE task is, therefore, to learn which
visual cues in the image can be used for depth estimation, and how. With
training data limited by cost of annotation or network capacity limited by
computational power, this is challenging. In this work we demonstrate that
explicitly injecting visual cue information into the model is beneficial for
depth estimation. Following research into biological vision systems, we focus
on semantic information and prior knowledge of object sizes and their
relations, to emulate the biological cues of relative size, familiar size, and
absolute size. We use state-of-the-art semantic and instance segmentation
models to provide external information, and exploit language embeddings to
encode relational information between classes. We also provide a prior on the
average real-world size of objects. This external information overcomes the
limitation in data availability, and ensures that the limited capacity of a
given network is focused on known-helpful cues, therefore improving
performance. We experimentally validate our hypothesis and evaluate the
proposed model on the widely used NYUD2 indoor depth estimation benchmark. The
results show improvements in depth prediction when the semantic information,
size prior and instance size are explicitly provided along with the RGB images,
and our method can be easily adapted to any depth estimation system.
