Automated extraction of structural planes and geometric information from point cloud

Laser scanning (LS) is a powerful tool for acquiring geometric information about structures. However, they face challenges in capturing targets and computing edges. This paper presents a method for automatically identifying point cloud (PC) planes and obtaining geometric information, which can provide construction quality. The proposed method consists of two parts. First, an iterative growth algorithm is introduced to identify multiple planes simultaneously with fewer parameters, which improves upon the traditional plane detection method that needs better robustness due to the abundance of parameters. Second, a framework is introduced for computing geometric information of PC planes, which integrates a k-dimensional tree to replace local density in the density peak clustering algorithm and computes edges while considering mixed points, significantly reducing the time and increasing precision. The effectiveness and accuracy of the method are verified by structural geometric information in the construction process.