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.

作为城市场景道路环境中的动态和必不可少的组成部分，车辆是最受欢迎的调查对象。为了监视车辆的行为并提取其几何特征，准确，即时地测量车辆在交通和运输领域起着至关重要的作用。从移动激光扫描（MLS）系统获取的点云可提供道路场景的3D信息，具有前所未有的细节。在智能交通和自动驾驶领域，尤其是提取车辆方面，它们已被证明是足够的数据源。但是，由于对象遮挡或自遮挡，从MLS系统获取的车辆3D点云不可避免地是不完整的。为了解决这个问题，我们提出了一种神经网络，可以根据MLS数据合成车辆的完整，密集和统一的点云，名为“车辆积分完成网”（VPC-Net）。在该网络中，我们引入了一个新的编码器模块，该模块从输入实例中提取全局特征，包括空间变换器网络和点特征增强层。此外，还提出了一个新的优化程序模块，用于从输入中保留车辆的详细信息，并使用细粒度的信息来优化完整的输出。给定稀疏和部分点云作为输入，该网络可以生成完整而逼真的车辆结构，并保留部分输入的细粒度细节。我们使用合成和实时扫描数据集在不同的实验中评估了拟议的VPC-Net，并将结果应用于3D车辆监控任务。定量和定性实验证明了所提出的VPC-Net的有希望的性能，并显示了最新的结果。