  The paper proposes an image-guided depth completion method to estimate
accurate dense depth maps with fast computation time. The proposed network has
two-stage structure. The first stage predicts a first depth map. Then, the
second stage further refines the first depth map using the confidence maps. The
second stage consists of two layers, each of which focuses on different regions
and generates a refined depth map and a confidence map. The final depth map is
obtained by combining two depth maps from the second stage using the
corresponding confidence maps. Compared with the top-ranked models on the KITTI
depth completion online leaderboard, the proposed model shows much faster
computation time and competitive performance.
