  Estimating a dense and accurate depth map is the key requirement for
autonomous driving and robotics. Recent advances in deep learning have allowed
depth estimation in full resolution from a single image. Despite this
impressive result, many deep-learning-based monocular depth estimation (MDE)
algorithms have failed to keep their accuracy yielding a meter-level estimation
error. In many robotics applications, accurate but sparse measurements are
readily available from Light Detection and Ranging (LiDAR). Although they are
highly accurate, the sparsity limits full resolution depth map reconstruction.
Targeting the problem of dense and accurate depth map recovery, this paper
introduces the fusion of these two modalities as a depth completion (DC)
problem by dividing the role of depth inference and depth regression. Utilizing
the state-of-the-art MDE and our Gaussian process (GP) based depth-regression
method, we propose a general solution that can flexibly work with various MDE
modules by enhancing its depth with sparse range measurements. To overcome the
major limitation of GP, we adopt Kernel Interpolation for Scalable Structured
(KISS)-GP and mitigate the computational complexity from O(N^3) to O(N). Our
experiments demonstrate that the accuracy and robustness of our method
outperform state-of-the-art unsupervised methods for sparse and biased
measurements.
