  Depth completion from sparse LiDAR and high-resolution RGB data is one of the
foundations for autonomous driving techniques. Current approaches often rely on
CNN-based methods with several known drawbacks: flying pixel at depth
discontinuities, overfitting to both a given data set as well as error metric,
and many more. Thus, we propose our novel Piecewise Depth Completion (PDC),
which works completely without deep learning. PDC segments the RGB image into
superpixels corresponding the regions with similar depth value. Superpixels
corresponding to same objects are gathered using a cost map. At the end, we
receive detailed depth images with state of the art accuracy. In our
evaluation, we can show both the influence of the individual proposed
processing steps and the overall performance of our method on the challenging
KITTI dataset.

Computer Vision and Pattern Recognition