  Inferring the depth of images is a fundamental inverse problem within the
field of Computer Vision since depth information is obtained through 2D images,
which can be generated from infinite possibilities of observed real scenes.
Benefiting from the progress of Convolutional Neural Networks (CNNs) to explore
structural features and spatial image information, Single Image Depth
Estimation (SIDE) is often highlighted in scopes of scientific and
technological innovation, as this concept provides advantages related to its
low implementation cost and robustness to environmental conditions. In the
context of autonomous vehicles, state-of-the-art CNNs optimize the SIDE task by
producing high-quality depth maps, which are essential during the autonomous
navigation process in different locations. However, such networks are usually
supervised by sparse and noisy depth data, from Light Detection and Ranging
(LiDAR) laser scans, and are carried out at high computational cost, requiring
high-performance Graphic Processing Units (GPUs). Therefore, we propose a new
lightweight and fast supervised CNN architecture combined with novel feature
extraction models which are designed for real-world autonomous navigation. We
also introduce an efficient surface normals module, jointly with a simple
geometric 2.5D loss function, to solve SIDE problems. We also innovate by
incorporating multiple Deep Learning techniques, such as the use of
densification algorithms and additional semantic, surface normals and depth
information to train our framework. The method introduced in this work focuses
on robotic applications in indoor and outdoor environments and its results are
evaluated on the competitive and publicly available NYU Depth V2 and KITTI
Depth datasets.
