Three-dimensional point cloud segmentation and inverse parametric modeling method based on comprehensive features

Aiming at the problem of 3D point cloud data segmentation and inverse parametric modeling, this paper proposes a segmentation algorithm and inverse parametric modeling technology using the spatial, geometric and statistical features of the point cloud. The algorithm divides the point cloud data by measuring distance, calculating curvature and estimating normal vector, and designs corresponding anti-parametric modeling techniques for different subsets. The experimental results show that the proposed algorithm and technique can achieve good results on multiple data sets and have good generalization ability. Through this research, it is expected to promote the development of 3D point cloud processing technology, and provide more accurate and efficient solutions for computer vision, automatic driving, reverse engineering and other fields. Future research directions include optimization of algorithm performance, improvement of generalization ability, and extension to support more geometric models.