  Depth Completion can produce a dense depth map from a sparse input and
provide a more complete 3D description of the environment. Despite great
progress made in depth completion, the sparsity of the input and low density of
the ground truth still make this problem challenging. In this work, we propose
DenseLiDAR, a novel real-time pseudo-depth guided depth completion neural
network. We exploit dense pseudo-depth map obtained from simple morphological
operations to guide the network in three aspects: (1) Constructing a residual
structure for the output; (2) Rectifying the sparse input data; (3) Providing
dense structural loss for training the network. Thanks to these novel designs,
higher performance of the output could be achieved. In addition, two new
metrics for better evaluating the quality of the predicted depth map are also
presented. Extensive experiments on KITTI depth completion benchmark suggest
that our model is able to achieve the state-of-the-art performance at the
highest frame rate of 50Hz. The predicted dense depth is further evaluated by
several downstream robotic perception or positioning tasks. For the task of 3D
object detection, 3~5 percent performance gains on small objects categories are
achieved on KITTI 3D object detection dataset. For RGB-D SLAM, higher accuracy
on vehicle's trajectory is also obtained in KITTI Odometry dataset. These
promising results not only verify the high quality of our depth prediction, but
also demonstrate the potential of improving the related downstream tasks by
using depth completion results.
