  Dense depth cues are important and have wide applications in various computer
vision tasks. In autonomous driving, LIDAR sensors are adopted to acquire depth
measurements around the vehicle to perceive the surrounding environments.
However, depth maps obtained by LIDAR are generally sparse because of its
hardware limitation. The task of depth completion attracts increasing
attention, which aims at generating a dense depth map from an input sparse
depth map. To effectively utilize multi-scale features, we propose three novel
sparsity-invariant operations, based on which, a sparsity-invariant multi-scale
encoder-decoder network (HMS-Net) for handling sparse inputs and sparse feature
maps is also proposed. Additional RGB features could be incorporated to further
improve the depth completion performance. Our extensive experiments and
component analysis on two public benchmarks, KITTI depth completion benchmark
and NYU-depth-v2 dataset, demonstrate the effectiveness of the proposed
approach. As of Aug. 12th, 2018, on KITTI depth completion leaderboard, our
proposed model without RGB guidance ranks first among all peer-reviewed methods
without using RGB information, and our model with RGB guidance ranks second
among all RGB-guided methods.
