  As a dynamic and essential component in the road environment of urban
scenarios, vehicles are the most popular investigation targets. To monitor
their behavior and extract their geometric characteristics, an accurate and
instant measurement of vehicles plays a vital role in traffic and
transportation fields. Point clouds acquired from the mobile laser scanning
(MLS) system deliver 3D information of road scenes with unprecedented detail.
They have proven to be an adequate data source in the fields of intelligent
transportation and autonomous driving, especially for extracting vehicles.
However, acquired 3D point clouds of vehicles from MLS systems are inevitably
incomplete due to object occlusion or self-occlusion. To tackle this problem,
we proposed a neural network to synthesize complete, dense, and uniform point
clouds for vehicles from MLS data, named Vehicle Points Completion-Net
(VPC-Net). In this network, we introduce a new encoder module to extract global
features from the input instance, consisting of a spatial transformer network
and point feature enhancement layer. Moreover, a new refiner module is also
presented to preserve the vehicle details from inputs and refine the complete
outputs with fine-grained information. Given sparse and partial point clouds as
inputs, the network can generate complete and realistic vehicle structures and
keep the fine-grained details from the partial inputs. We evaluated the
proposed VPC-Net in different experiments using synthetic and real-scan
datasets and applied the results to 3D vehicle monitoring tasks. Quantitative
and qualitative experiments demonstrate the promising performance of the
proposed VPC-Net and show state-of-the-art results.
