Analysis And Research On 3D Point Cloud Segmentation Algorithm Based On Improved Euclidean Distance

With the rapid development of computer vision technology,more and more attention has been paid to 3D model reconstruction.3D point cloud segmentation plays a connecting role in the field of 3D model reconstruction,and the segmentation results will directly affect the accuracy of subsequent 3D model reconstruct ion.Based on the theory of Euclidean distance based 3D point cloud segmentation algorithm,combined with the 3D point cloud of overhead transmission line,on the basis of voxel filtering,this paper deeply studies the 3D point cloud segmentation of overhead transmission line.The main work of this paper is as follows :(1)This paper briefly analyzed the research significance of the subject and the research status of 3D point cloud segmentation algorithm at home and abroad,classified and analyzed the current 3D point cloud segmentation algorithm,compared the advantages and disadvantages and application scope of various algorithms.This paper summarized the characteristics and technical advantages of the point cloud filtering algorithm,and took the three-dimensional point cloud of overhead transmission line as the object to carry out the filt ering experiment,which verified the feasibility of the filtering algorithm.(2)This paper studied the algorithm of 3D point cloud segmentation based on Euclidean distance,analyzed the principle of the algorithm and the characteristics of various algorithms.Based on Euclidean distance of 3D point cloud,combined with K-means clustering algorithm and region growing algorithm,3D point cloud of overhead transmission line is segmented.Through the experimental results,the performance of the two algorithms is compared,the shortcomings of the algorithm are analyzed,and the improvement scheme is explored.(3)Aiming at the defects of the above two Euclidean dist ance based 3D point cloud segmentation algorithms,an improved algorithm is proposed.Firstly,a six nearest neighbor triangulation fitting method is used to estimate the normal vector of the point cloud.Secondly,the smooth point with the minimum curvatu re is selected as the seed point.The Euclidean distance and the angle between the normal vectors are used as the region growth threshold to improve the requirement of region growth and make the points in the same class have higher similarity.Finally,the region growth is used to segment the target point cloud.The experimental results show that the improved segmentation algorithm has good segmentation effect on 3D point cloud,especially for the point cloud of the connected part of the object.