  Depth completion aims to predict a dense depth map from a sparse depth input.
The acquisition of dense ground truth annotations for depth completion settings
can be difficult and, at the same time, a significant domain gap between real
LiDAR measurements and synthetic data has prevented from successful training of
models in virtual settings. We propose a domain adaptation approach for
sparse-to-dense depth completion that is trained from synthetic data, without
annotations in the real domain or additional sensors. Our approach simulates
the real sensor noise in an RGB+LiDAR set-up, and consists of three modules:
simulating the real LiDAR input in the synthetic domain via projections,
filtering the real noisy LiDAR for supervision and adapting the synthetic RGB
image using a CycleGAN approach. We extensively evaluate these modules against
the state-of-the-art in the KITTI depth completion benchmark, showing
significant improvements.
