The quality of 3D models of existing buildings reconstructed from point clouds is strongly related to the segmentation process used to detect structural elements. A new wall detection method in the indoor point clouds of buildings is presented in this study. The point clouds are segmented into horizontal layers, and a concept of continuous segments in a 2D grid representation is used to extract the footprints of the wall structures, and 2D blocks are projected into 3D space to obtain the wall segments in the initial 3D point cloud. The results obtained from the execution of the proposed method demonstrate that wall blocks in indoor point clouds are detected independently of their shape. Executing the proposed method on a set of 9 in-door point clouds revealed better performance in terms of result quality and execution time compared to RANSAC. The robustness of the method can be improved by adding a classification step to eliminate non-consistent blocks.

从点云重建的现有建筑物的 3D 模型的质量与用于检测结构元素的分割过程密切相关。本研究提出了一种新的建筑物室内点云墙体检测方法。点云被分割成水平层，并使用2D网格表示中连续段的概念来提取墙体结构的足迹，将2D块投影到3D空间中以获得初始3D点云中的墙段. 从所提出的方法的执行中获得的结果表明，室内点云中的墙块的检测与其形状无关。与 RANSAC 相比，在一组 9 个室内点云上执行所提出的方法在结果质量和执行时间方面表现出更好的性能。