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As a dynamic and essential component in the road environment of urbanscenarios, vehicles are the most popular investigation targets. To monitortheir behavior and extract their geometric characteristics, an accurate andinstant measurement of vehicles plays a vital role in traffic andtransportation 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 intelligenttransportation and autonomous driving, especially for extracting vehicles.However, acquired 3D point clouds of vehicles from MLS systems are inevitablyincomplete due to object occlusion or self-occlusion. To tackle this problem,we proposed a neural network to synthesize complete, dense, and uniform pointclouds for vehicles from MLS data, named Vehicle Points Completion-Net(VPC-Net). In this network, we introduce a new encoder module to extract globalfeatures from the input instance, consisting of a spatial transformer networkand point feature enhancement layer. Moreover, a new refiner module is alsopresented to preserve the vehicle details from inputs and refine the completeoutputs with fine-grained information. Given sparse and partial point clouds asinputs, the network can generate complete and realistic vehicle structures andkeep the fine-grained details from the partial inputs. We evaluated theproposed VPC-Net in different experiments using synthetic and real-scandatasets and applied the results to 3D vehicle monitoring tasks. Quantitativeand qualitative experiments demonstrate the promising performance of theproposed VPC-Net and show state-of-the-art results.

VPC-Net: Completion of 3D Vehicles from MLS Point Clouds