  Depth perception is fundamental for robots to understand the surrounding
environment. As the view of cognitive neuroscience, visual depth perception
methods are divided into three categories, namely binocular, active, and
pictorial. The first two categories have been studied for decades in detail.
However, research for the exploration of the third category is still in its
infancy and has got momentum by the advent of deep learning methods in recent
years. In cognitive neuroscience, it is known that pictorial depth perception
mechanisms are dependent on the perception of seen objects. Inspired by this
fact, in this thesis, we investigated the relation of perception of objects and
depth estimation convolutional neural networks. For this purpose, we developed
new network structures based on a simple depth estimation network that only
used a single image at its input. Our proposed structures use both an image and
a semantic label of the image as their input. We used semantic labels as the
output of object perception. The obtained results of performance comparison
between the developed network and original network showed that our novel
structures can improve the performance of depth estimation by 52\% of relative
error of distance in the examined cases. Most of the experimental studies were
carried out on synthetic datasets that were generated by game engines to
isolate the performance comparison from the effect of inaccurate depth and
semantic labels of non-synthetic datasets. It is shown that particular
synthetic datasets may be used for training of depth networks in cases that an
appropriate dataset is not available. Furthermore, we showed that in these
cases, usage of semantic labels improves the robustness of the network against
domain shift from synthetic training data to non-synthetic test data.
