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Automatic Vehicle Extraction from Airborne LiDAR Data Using an Object-Based Point Cloud Analysis Method

机译：基于对象的点云分析方法从机载LiDAR数据中自动提取车辆

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摘要

Automatic vehicle extraction from an airborne laser scanning (ALS) point cloud is very useful for many applications, such as digital elevation model generation and 3D building reconstruction. In this article, an object-based point cloud analysis (OBPCA) method is proposed for vehicle extraction from an ALS point cloud. First, a segmentation-based progressive TIN (triangular irregular network) densification is employed to detect the ground points, and the potential vehicle points are detected based on the normalized heights of the non-ground points. Second, 3D connected component analysis is performed to group the potential vehicle points into segments. At last, vehicle segments are detected based on three features, including area, rectangularity and elongatedness. Experiments suggest that the proposed method is capable of achieving higher accuracy than the exiting mean-shift-based method for vehicle extraction from an ALS point cloud. Moreover, the larger the point density is, the higher the achieved accuracy is.
机译：从机载激光扫描（ALS）点云中自动提取车辆对于许多应用程序非常有用，例如数字高程模型生成和3D建筑物重建。本文提出了一种基于对象的点云分析（OBPCA）方法，用于从ALS点云中提取车辆。首先，采用基于分段的渐进式TIN（三角不规则网络）致密化来检测地面点，并基于非地面点的标准化高度来检测潜在的车辆点。其次，执行3D连接的零部件分析以将潜在的车辆点分组。最后，基于面积，矩形和伸长率三个特征来检测车辆分段。实验表明，与现有的基于均值平移的方法从ALS点云中提取车辆相比，该方法能够实现更高的精度。此外，点密度越大，则获得的精度越高。

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