Complete depth information and efficient estimators have become vital
ingredients in scene understanding for automated driving tasks. A major problem
for LiDAR-based depth completion is the inefficient utilization of convolutions
due to the lack of coherent information as provided by the sparse nature of
uncorrelated LiDAR point clouds, which often leads to complex and
resource-demanding networks. The problem is reinforced by the expensive
aquisition of depth data for supervised training. In this work, we propose an
efficient depth completion model based on a vgg05-like CNN architecture and
propose a semi-supervised domain adaptation approach to transfer knowledge from
synthetic to real world data to improve data-efficiency and reduce the need for
a large database. In order to boost spatial coherence, we guide the learning
process using segmentations as additional source of information. The efficiency
and accuracy of our approach is evaluated on the KITTI dataset. Our approach
improves on previous efficient and low parameter state of the art approaches
while having a noticeably lower computational footprint