  Image guided depth completion is the task of generating a dense depth map
from a sparse depth map and a high quality image. In this task, how to fuse the
color and depth modalities plays an important role in achieving good
performance. This paper proposes a two-branch backbone that consists of a
color-dominant branch and a depth-dominant branch to exploit and fuse two
modalities thoroughly. More specifically, one branch inputs a color image and a
sparse depth map to predict a dense depth map. The other branch takes as inputs
the sparse depth map and the previously predicted depth map, and outputs a
dense depth map as well. The depth maps predicted from two branches are
complimentary to each other and therefore they are adaptively fused. In
addition, we also propose a simple geometric convolutional layer to encode 3D
geometric cues. The geometric encoded backbone conducts the fusion of different
modalities at multiple stages, leading to good depth completion results. We
further implement a dilated and accelerated CSPN++ to refine the fused depth
map efficiently. The proposed full model ranks 1st in the KITTI depth
completion online leaderboard at the time of submission. It also infers much
faster than most of the top ranked methods. The code of this work is available
at https://github.com/JUGGHM/PENet_ICRA2021.
