  High resolution depth-maps, obtained by upsampling sparse range data from a
3D-LIDAR, find applications in many fields ranging from sensory perception to
semantic segmentation and object detection. Upsampling is often based on
combining data from a monocular camera to compensate the low-resolution of a
LIDAR. This paper, on the other hand, introduces a novel framework to obtain
dense depth-map solely from a single LIDAR point cloud; which is a research
direction that has been barely explored. The formulation behind the proposed
depth-mapping process relies on local spatial interpolation, using
sliding-window (mask) technique, and on the Bilateral Filter (BF) where the
variable of interest, the distance from the sensor, is considered in the
interpolation problem. In particular, the BF is conveniently modified to
perform depth-map upsampling such that the edges (foreground-background
discontinuities) are better preserved by means of a proposed method which
influences the range-based weighting term. Other methods for spatial upsampling
are discussed, evaluated and compared in terms of different error measures.
This paper also researches the role of the mask's size in the performance of
the implemented methods. Quantitative and qualitative results from experiments
on the KITTI Database, using LIDAR point clouds only, show very satisfactory
performance of the approach introduced in this work.
