  Accurate dense depth estimation is crucial for autonomous vehicles to analyze
their environment. This paper presents a non-deep learning-based approach to
densify a sparse LiDAR-based depth map using a guidance RGB image. To achieve
this goal the RGB image is at first cleared from most of the camera-LiDAR
misalignment artifacts. Afterward, it is over segmented and a plane for each
superpixel is approximated. In the case a superpixel is not well represented by
a plane, a plane is approximated for a convex hull of the most inlier. Finally,
the pinhole camera model is used for the interpolation process and the
remaining areas are interpolated. The evaluation of this work is executed using
the KITTI depth completion benchmark, which validates the proposed work and
shows that it outperforms the state-of-the-art non-deep learning-based methods,
in addition to several deep learning-based methods.
