  Perceiving 3D information is of paramount importance in many applications of
computer vision. Recent advances in monocular depth estimation have shown that
gaining such knowledge from a single camera input is possible by training deep
neural networks to predict inverse depth and pose, without the necessity of
ground truth data. The majority of such approaches, however, require camera
parameters to be fed explicitly during training. As a result, image sequences
from wild cannot be used during training. While there exist methods which also
predict camera intrinsics, their performance is not on par with novel methods
taking camera parameters as input. In this work, we propose a method for
implicit estimation of pinhole camera intrinsics along with depth and pose, by
learning from monocular image sequences alone. In addition, by utilizing
efficient sub-pixel convolutions, we show that high fidelity depth estimates
can be obtained. We also embed pixel-wise uncertainty estimation into the
framework, to emphasize the possible applicability of this work in practical
domain. Finally, we demonstrate the possibility of accurate prediction of depth
information without prior knowledge of camera intrinsics, while outperforming
the existing state-of-the-art approaches on KITTI benchmark.
