  This paper presents a novel CNN-based approach for synthesizing
high-resolution LiDAR point cloud data. Our approach generates semantically and
perceptually realistic results with guidance from specialized loss-functions.
First, we utilize a modified per-point loss that addresses missing LiDAR point
measurements. Second, we align the quality of our generated output with
real-world sensor data by applying a perceptual loss. In large-scale
experiments on real-world datasets, we evaluate both the geometric accuracy and
semantic segmentation performance using our generated data vs. ground truth. In
a mean opinion score testing we further assess the perceptual quality of our
generated point clouds. Our results demonstrate a significant quantitative and
qualitative improvement in both geometry and semantics over traditional non
CNN-based up-sampling methods.
