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A Methodology for Automated Segmentation and Reconstruction of Urban 3-D Buildings from ALS Point Clouds

机译:一种基于ALS点云的城市3-D建筑物自动分割和重建的方法

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In this paper, a methodology which allows automated and efficient reconstruction of three-dimensional (3-D) geometric building models from an Airborne Laser Scanning (ALS) point cloud is introduced and its performance is analyzed and evaluated. The proposed method avoids abnormal and/or infinite solutions which are typically encountered in previously published methods that use the rooftop primitive adjacency matrix to solve the critical rooftop vertices. In particular, first, an improved random sample consensus (RANSAC) algorithm is proposed to segment the rooftop primitives, i.e., the planar patches that constitute rooftops, of each building or group of connected buildings. The algorithm successfully maintains topological consistency among primitives and avoids under- and over-segmentation with high efficiency. Second, a novel Voronoi-based primitive boundary extraction algorithm under constraints of outer and inner building boundaries is introduced in order to extract each primitive boundary. In this algorithm, the adjacent segmented primitive relationships among the various primitives are preserved by a subgraph of the Voronoi diagram so that the reconstructed neighbor primitives are seamlessly connected. Third, in order to refine the boundary shapes of primitives with irregular geometry, various criteria for making the boundary adjustments more effective are proposed. In this way, more regular 3-D buildings can be produced. Finally, the primitive boundary simplification criteria are formally introduced to generate compact 3-D building models. By using the simplification criteria, nonadjacency between neighbor primitives, intersection between boundaries, and self-intersections are, to a great extent, avoided. Numerous experimental results obtained using multiple data sets, including data from the cities of Toronto and Enschede as well as from the Niagara area, have shown that the proposed methodology has excellent performance and it can produce watertight 3-D po- yhedral building models.
机译:在本文中,介绍了一种方法,该方法可以从机载激光扫描(ALS)点云中自动高效地重建三维(3-D)几何建筑模型,并对其性能进行分析和评估。所提出的方法避免了在使用屋顶原始邻接矩阵来解决关键屋顶顶点的先前公开的方法中通常遇到的异常和/或无限解。特别地,首先,提出了一种改进的随机样本共识(RANSAC)算法,以分割每个建筑物或相连建筑物的屋顶图元,即构成屋顶的平面补丁。该算法成功地保持了基元之间的拓扑一致性,并高效避免了分段不足和分段过多。其次,介绍了一种在建筑物内部和外部边界约束下基于Voronoi的新颖原始边界提取算法,以提取每个原始边界。在该算法中,通过Voronoi图的子图保留了各种图元之间的相邻分段图元关系,从而将重建的相邻图元无缝连接。第三,为了细化具有不规则几何形状的图元的边界形状,提出了各种使边界调整更有效的准则。这样,可以生产出更常规的3D建筑。最后,正式引入原始边界简化标准以生成紧凑的3D建筑模型。通过使用简化标准,可以在很大程度上避免相邻图元之间的不相邻,边界之间的相交以及自相交。使用多个数据集获得的大量实验结果,包括来自多伦多市和恩斯赫德市以及尼亚加拉地区的数据,表明所提出的方法具有出色的性能,并且可以生成防水的3-D多面体建筑模型。

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