  Robust perception systems are essential for autonomous vehicle safety. To
navigate in a complex urban environment, it is necessary precise sensors with
reliable data. The task of understanding the surroundings is hard by itself;
for intelligent vehicles, it is even more critical due to the high speed in
which the vehicle navigates. To successfully navigate in an urban environment,
the perception system must quickly receive, process, and execute an action to
guarantee both passenger and pedestrian safety. Stereo cameras collect
environment information at many levels, e.g., depth, color, texture, shape,
which guarantee ample knowledge about the surroundings. Even so, when compared
to human, computational methods lack the ability to deal with missing
information, i.e., occlusions. For many perception tasks, this lack of data can
be a hindrance due to the environment incomplete information. In this paper, we
address this problem and discuss recent methods to deal with occluded areas
inference. We then introduce a loss function focused on disparity and
environment depth data reconstruction, and a Generative Adversarial Network
(GAN) architecture able to deal with occluded information inference. Our
results present a coherent reconstruction on depth maps, estimating regions
occluded by different obstacles. Our final contribution is a loss function
focused on disparity data and a GAN able to extract depth features and estimate
depth data by inpainting disparity images.
