  While monocular depth estimation (MDE) is an important problem in computer
vision, it is difficult due to the ambiguity that results from the compression
of a 3D scene into only 2 dimensions. It is common practice in the field to
treat it as simple image-to-image translation, without consideration for the
semantics of the scene and the objects within it. In contrast, humans and
animals have been shown to use higher-level information to solve MDE: prior
knowledge of the nature of the objects in the scene, their positions and likely
configurations relative to one another, and their apparent sizes have all been
shown to help resolve this ambiguity.
  In this paper, we present a novel method to enhance MDE performance by
encouraging use of known-useful information about the semantics of objects and
inter-object relationships within a scene. Our novel ObjCAViT module sources
world-knowledge from language models and learns inter-object relationships in
the context of the MDE problem using transformer attention, incorporating
apparent size information. Our method produces highly accurate depth maps, and
we obtain competitive results on the NYUv2 and KITTI datasets. Our ablation
experiments show that the use of language and cross-attention within the
ObjCAViT module increases performance. Code is released at
https://github.com/DylanAuty/ObjCAViT.
