激光点云大数据下的建筑空间可视化重建方法

Xiyin Ma, Jian Li
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摘要

为了解决建筑空间重建过程中由于点云数据量大而影响重建精度和完整性的问题,研究了激光点云大数据下的建筑空间可视化重建方法。利用三维激光扫描仪采集建筑空间中的激光点云大数据,通过点云金字塔分层计算、稀疏化处理和块状处理三个步骤对激光点云大数据进行整理和处理。从激光点云大数据的处理结果中,基于改进的均值平移法提取建筑空间的线特征,并利用双半径阈值线描法提取建筑空间点云数据中的连续断线。根据建筑空间点云数据的特征提取结果,通过平移匹配和空间匹配的过程完成建筑空间的视觉重建。实验结果表明,该方法可以实现建筑空间的可视化重建,平均重建精度高于 97%,重建完成度和平滑度均高于 95%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Visual reconstruction method of architectural space under laser point cloud big data
In order to solve the problem that the reconstruction accuracy and integrity are affected due to the large amount of point cloud data in the process of building space reconstruction, the visual reconstruction method of building space under laser point cloud big data is studied. The three-dimensional laser scanner is used to collect the laser point cloud big data in the building space, and the laser point cloud big data is organized and processed through three steps: hierarchical calculation of the point cloud pyramid, thinning treatment and block treatment. From the processing results of laser point cloud big data, the line features of building space are extracted based on the improved Mean-shift method, and the continuous broken lines in the point cloud data of building space are extracted by using the double radius threshold line tracing method. According to the feature extraction results of point cloud data in building space, the visual reconstruction of building space is completed through the process of translation matching and space matching. The experimental results show that this method can realize the visual reconstruction of architectural space, and the average reconstruction accuracy is higher than that of 97 %, and the reconstruction completion and smoothness are higher than 95 %.
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