  We propose a method for depth estimation under different illumination
conditions, i.e., day and night time. As photometry is uninformative in regions
under low-illumination, we tackle the problem through a multi-sensor fusion
approach, where we take as input an additional synchronized sparse point cloud
(i.e., from a LiDAR) projected onto the image plane as a sparse depth map,
along with a camera image. The crux of our method lies in the use of the
abundantly available synthetic data to first approximate the 3D scene structure
by learning a mapping from sparse to (coarse) dense depth maps along with their
predictive uncertainty - we term this, SpaDe. In poorly illuminated regions
where photometric intensities do not afford the inference of local shape, the
coarse approximation of scene depth serves as a prior; the uncertainty map is
then used with the image to guide refinement through an uncertainty-driven
residual learning (URL) scheme. The resulting depth completion network
leverages complementary strengths from both modalities - depth is sparse but
insensitive to illumination and in metric scale, and image is dense but
sensitive with scale ambiguity. SpaDe can be used in a plug-and-play fashion,
which allows for 25% improvement when augmented onto existing methods to
preprocess sparse depth. We demonstrate URL on the nuScenes dataset where we
improve over all baselines by an average 11.65% in all-day scenarios, 11.23%
when tested specifically for daytime, and 13.12% for nighttime scenes.
