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Abstract：
This paper presents a novel segmentation algorithm specially developed for applications in 3D point clouds with high variability and noise, particularly suitable for heritage building 3D data. The method can be categorized within the segmentation procedures based on edge detection. In addition, it uses a graph-based topological structure generated from the supervoxelization of the 3D point clouds, which is used to make the closure of the edge points and to define the different segments. The algorithm provides a valuable tool for generating results that can be used in subsequent classification tasks and broader computer applications dealing with 3D point clouds. One of the characteristics of this segmentation method is that it is unsupervised, which makes it particularly advantageous for heritage applications where labelled data is scarce. It is also easily adaptable to different edge point detection and supervoxelization algorithms. Finally, the results show that the 3D data can be segmented into different architectural elements, which is important for further classification or recognition. Extensive testing on real data from historic buildings demonstrated the effectiveness of the method. The results show superior performance compared to three other segmentation methods, both globally and in the segmentation of planar and curved zones of historic buildings.
摘要：
本文提出了一种新颖的分割算法，专门为具有高变异性和噪声的3D点云应用而开发，特别适用于文物建筑的三维数据。该方法可以在基于边缘检测的分割过程中进行分类。此外,它使用从3D点云的超体素化生成的基于图形的拓扑结构，用于使边缘点闭合并定义不同的段。该算法为生成结果提供了有价值的工具，这些结果可用于后续的分类任务和处理3D点云的更广泛的计算机应用。这种分割方法的特点之一是它是无监督的，这使得它对于标记数据稀缺的传统应用特别有利。它也很容易适应不同的边缘点检测和超体素化算法。最后,结果表明，三维数据可以分割成不同的建筑元素，这对于进一步分类或识别很重要。对历史建筑的真实数据进行的大量测试证明了该方法的有效性。结果表明，与其他三种分割方法相比，性能更优越，无论是在全球范围内还是在历史建筑的平面和弯曲区域的分割中。
