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.

该研究提出了一个神经网络模型，命名为 VPC-Net，用于从移动激光扫描系统获取的点云数据中合成完整、密集、统一的车辆点云数据，以用于 3D 车辆监测任务，具有良好的性能表现与结果。

VPC-Net：基于 MLS 点云的三维车辆完成

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

This paper introduces a new Urban Point Cloud Dataset for Automatic
Segmentation and Classification acquired by Mobile Laser Scanning (MLS). We
describe how the dataset is obtained from acquisition to post-processing and
labeling. This dataset can be used to learn classification algorithm, however,
given that a great attention has been paid to the split between the different
objects, this dataset can also be used to learn the segmentation. The dataset
consists of around 2km of MLS point cloud acquired in two cities. The number of
points and range of classes make us consider that it can be used to train
Deep-Learning methods. Besides we show some results of automatic segmentation
and classification. The dataset is available at:
this http URL

本文介绍了一种基于移动激光扫描技术获得的城市点云自动分割和分类新数据集，可用于深度学习方法的训练。