Extraction of Features of Regular Surfaces from the Laser Point Clouds for 3D Objects

Abstract

A fusion optimization algorithm has been proposed to enhance the reliability and accuracy of regular surface feature extraction from laser point clouds. to get optimal result. Firstly, the Octree-based constrained adaptive growth method is utilized to optimize the neighborhood points of point cloud and establish its topological relationship. Secondly, the Harris-3D algorithm is applied to extract key points from the point cloud data, followed by a region growth method that combines double thresholds of normal vector angle and Euclidean distance, to segment the point cloud into separate clusters. Finally, regular surface features are extracted from these clusters, allowing for the recognition of 3D object surface morphology and features. Experiments on regular surface feature extraction from point clouds have shown that the proposed fusion optimization algorithm can significantly improve the accuracy and efficiency of feature extraction. The RMS errors for the extraction and reconstruction of quadric surfaces like planes, cylinders, cones, and spheres are below 0.020 mm. Additionally, a real-world experiment involving a large amount of complex point cloud data from an unmanned laser scanning scene also confirms the effectiveness of the proposed feature extraction optimization algorithm for regular surface feature extraction, object recognition, and 3D reconstruction.