  Depth completion is the task of recovering dense depth maps from sparse ones,
usually with the help of color images. Existing image-guided methods perform
well on daytime depth perception self-driving benchmarks, but struggle in
nighttime scenarios with poor visibility and complex illumination. To address
these challenges, we propose a simple yet effective framework called LDCNet.
Our key idea is to use Recurrent Inter-Convolution Differencing (RICD) and
Illumination-Affinitive Intra-Convolution Differencing (IAICD) to enhance the
nighttime color images and reduce the negative effects of the varying
illumination, respectively. RICD explicitly estimates global illumination by
differencing two convolutions with different kernels, treating the
small-kernel-convolution feature as the center of the large-kernel-convolution
feature in a new perspective. IAICD softly alleviates local relative light
intensity by differencing a single convolution, where the center is dynamically
aggregated based on neighboring pixels and the estimated illumination map in
RICD. On both nighttime depth completion and depth estimation tasks, extensive
experiments demonstrate the effectiveness of our LDCNet, reaching the state of
the art.
