  Recent advances in mobile mapping systems have greatly enhanced the
efficiency and convenience of acquiring urban 3D data. These systems utilize
LiDAR sensors mounted on vehicles to capture vast cityscapes. However, a
significant challenge arises due to occlusions caused by roadside parked
vehicles, leading to the loss of scene information, particularly on the roads,
sidewalks, curbs, and the lower sections of buildings. In this study, we
present a novel approach that leverages deep neural networks to learn a model
capable of filling gaps in urban scenes that are obscured by vehicle occlusion.
We have developed an innovative technique where we place virtual vehicle models
along road boundaries in the gap-free scene and utilize a ray-casting algorithm
to create a new scene with occluded gaps. This allows us to generate diverse
and realistic urban point cloud scenes with and without vehicle occlusion,
surpassing the limitations of real-world training data collection and
annotation. Furthermore, we introduce the Scene Gap Completion Network
(SGC-Net), an end-to-end model that can generate well-defined shape boundaries
and smooth surfaces within occluded gaps. The experiment results reveal that
97.66% of the filled points fall within a range of 5 centimeters relative to
the high-density ground truth point cloud scene. These findings underscore the
efficacy of our proposed model in gap completion and reconstructing urban
scenes affected by vehicle occlusions.
