  Depth estimation is an important capability for autonomous vehicles to
understand and reconstruct 3D environments as well as avoid obstacles during
the execution. Accurate depth sensors such as LiDARs are often heavy, expensive
and can only provide sparse depth while lighter depth sensors such as stereo
cameras are noiser in comparison. We propose an end-to-end learning algorithm
that is capable of using sparse, noisy input depth for refinement and depth
completion. Our model also produces the camera pose as a byproduct, making it a
great solution for autonomous systems. We evaluate our approach on both indoor
and outdoor datasets. Empirical results show that our method performs well on
the KITTI~\cite{kitti_geiger2012we} dataset when compared to other competing
methods, while having superior performance in dealing with sparse, noisy input
depth on the TUM~\cite{sturm12iros} dataset.
