  Estimating the relative depth of a scene is a significant step towards
understanding the general structure of the depicted scenery, the relations of
entities in the scene and their interactions. When faced with the task of
estimating depth without the use of Stereo images, we are dependent on the
availability of large-scale depth datasets and high-capacity models to capture
the intrinsic nature of depth. Unfortunately, creating datasets of depth images
is not a trivial task as the requirements for the camera mainly limits us to
areas where we can provide the necessities for the camera to work.
  In this work, we present a new depth dataset captured from Video Games in an
easy and reproducible way. The nature of open-world video games gives us the
ability to capture high-quality depth maps in the wild without the
constrictions of Stereo cameras. Experiments on this dataset shows that using
such synthetic datasets increases the accuracy of Monocular Depth Estimation in
the wild where other approaches usually fail to generalize.
