  As processing power has become more available, more human-like artificial
intelligences are created to solve image processing tasks that we are
inherently good at. As such we propose a model that estimates depth from a
monocular image. Our approach utilizes a combination of structure from motion
and stereo disparity. We estimate a pose between the source image and a
different viewpoint and a dense depth map and use a simple transformation to
reconstruct the image seen from said viewpoint. We can then use the real image
at that viewpoint to act as supervision to train out model. The metric chosen
for image comparison employs standard L1 and structural similarity and a
consistency constraint between depth maps as well as smoothness constraint. We
show that similar to human perception utilizing the correlation within the
provided data by two different approaches increases the accuracy and
outperforms the individual components.
