  Given the lidar measurements from an autonomous vehicle, we can project the
points and generate a sparse depth image. Depth completion aims at increasing
the resolution of such a depth image by infilling and interpolating the sparse
depth values. Like most existing approaches, we make use of camera images as
guidance in very sparse or occluded regions. In addition, we propose a temporal
algorithm that utilizes information from previous timesteps using recurrence.
In this work, we show how a state-of-the-art method PENet can be modified to
benefit from recurrency. Our algorithm achieves state-of-the-art results on the
KITTI depth completion dataset while adding only less than one percent of
additional overhead in terms of both neural network parameters and floating
point operations. The accuracy is especially improved for faraway objects and
regions containing a low amount of lidar depth samples. Even in regions without
any ground truth (like sky and rooftops) we observe large improvements which
are not captured by the existing evaluation metrics.
