Recent advancement of remote sensing technologies has brought in accurate, dense, and inexpensive city-scale Light Detection And Ranging (LiDAR) point clouds, which can be utilized to model city objects (e.g., buildings, roads, and automobiles) for creating Digital Twin Cities (DTCs). However, processing such unstructured point clouds is very challenging, epitomized by high cost, movable objects, limited object classes, and high information inadequacy/redundancy. We noticed that many city objects are not in random shapes; rather, they have invariant cross-sections following the Gestalt design principles, including proximity, connectivity, symmetry, and similarity. In this paper, we present a novel unsupervised method, called Clustering Of Symmetric Cross-sections of Objects (COSCO), to process urban LiDAR point clouds to a hierarchy of objects based on their characteristic cross-sections. First, city objects are segmented as connected patches of proximate 3D points. Then, symmetric cross-sections are detected for symmetric city objects. Finally, the taxonomy and groups of city objects are recognized from a hierarchical clustering analysis of the dissimilarity matrix. Experimental results showed that COSCO detected the correct taxonomy and types of 12 cars from 24,126 LiDAR points in 8.28 s. Based on the cross-sections and taxonomy, a digital twin was created by registering online free 3D car models in 29.58 s. The contribution of this paper is twofold. First, it presents an effective unsupervised method for understanding and developing DTC objects in LiDAR point clouds by harnessing innate Gestalt design principles. Secondly, COSCO can be an efficient LiDAR pre-processing tool for recognizing symmetric city objects’ cross-sections, positions, heading directions, dimensions, and possible types for smart city applications in GIScience, Architecture, Engineering, Construction and Operation (AECO), and autonomous vehicles.

遥感技术的最新进展带来了精确，密集且廉价的城市规模的光检测和测距（LiDAR）点云，可将其用于对城市对象（例如建筑物，道路和汽车）建模以创建Digital Twin城市（DTC）。但是，处理此类非结构化点云非常具有挑战性，其特点是成本高昂，可移动对象，对象类别有限以及信息不足/冗余高。我们注意到许多城市物体并不是随机形状的；相反，它们遵循格式塔设计原则的横截面不变，包括邻近性，连通性，对称性和相似性。在本文中，我们提出了一种新颖的无监督方法，称为对象对称横截面聚类（COSCO），根据其特征横截面将城市LiDAR点云处理为对象的层次结构。首先，将城市对象分割为3D点附近的相连小块。然后，为对称的城市对象检测对称的横截面。最后，从不同矩阵的层次聚类分析中识别出分类法和城市对象组。实验结果表明，中远集团在8.28 s内从24,126 LiDAR点中检测出12辆汽车的正确分类和类型。根据横截面和分类法，通过在29.58 s内注册在线免费3D汽车模型来创建数字孪生。本文的贡献是双重的。首先，它提出了一种有效的无监督方法，可以利用固有的格式塔设计原理来理解和开发LiDAR点云中的DTC对象。其次，