On Triangle Mesh Surface Reconstruction For Scattered Point Clouds

In recent years,the rapid development of laser scanning technique has made great progress on measuring techniques.The measuring techniques of 3D spatial information on the surface of an object provides a new way for surveying and mapping based on laser scanning technology.The results of laser scanning are presented in the form of point clouds.Surface reconstruction rely on point clouds has broad application prospects in reverse engineering,cultural heritage protection and smart city construction.To improving efficiency,reconstructing non-uniform point clouds and reconstructing sharp feature,this paper researches the triangle mesh surface reconstruction for scattered point clouds from laser scanner.The main innovation points are as follows:1.In order to meet the stringent requirements for reconstruction efficiency of massive point clouds,a fast surface reconstruction algorithm for point cloud based on 2D projection is designed.Point clouds in 3D space are transformed into points on 2D plane accoding to the local flatness of surface,and 2D Delaunay triangulation is done.The Delaunay neighbor points are selected and the topological information are mapped into 3D space.Removing non-manifold triangles to acquire correct reconstruction.The experimental results show that the proposed algorithm improves the efficiency of surface reconstruction and reduces the complexity of surface reconstruction.2.?-shape algorithm is not suitable for surface reconstruction of non-uniform point clouds.In order to solve the problem,an adaptive ?-shape surface reconstruction algorithm for non-uniform point clouds based on Local Feature Size is proposed.the LFS is more accurately calculated through the public pole points of k-nearest neighbors.The amount of point cloud is decreased and the redundant points are removed based on LFS.The mathematical relationship among ?,LFS and the distance between adjacent points is derived,by which dynamically adjust ? and adaptively reconstruct non-uniform point clouds.The experimental results show that this improved algorithm can adaptively reconstruct the surface of non-uniform point clouds.3.In order to improve the efficiency and accuracy of present normal vector adjustment algorithms,a consistent normal vector adjustment algorithm using surface variation is proposed.the surface variation of point clouds are calculated using principal component analysis,and the points on flat or uneven area are distinguished based on surface variation.the search scope is narrowed and the number of adjusted normal vector is increased to improve efficiency.The propagating direction of normal vector is restrained to insure accuracy.The experiments show that the proposed algorithm can always receive accurate result on flat region,feature condition and high curvature area,meanwhile,the proposed algorithm is more efficient than the present algorithms.4.In order to solve the drawback which the existing surface reconstruction algorithms is hard to reconstruct the sharp features of point cloud model,a point-line-surface gradual surface reconstruction algorithm with the sharp feature is proposed.The accurate vectors of point cloud was calculated by and k-nearest neighbors iterative weighting.Feature points are screen out by vector clustering and plane fitting from the shortlisted feature points.Feature lines are reconstructed according to the mutual relation between growing direction and principal direction of feature points.Meanwhile,corner points are optimized on the basis of least squares theory.Finally,surface is reconstructed by feature lines constraint.The experimental results show that the proposed algorithm can accurately estimates the vectors of point cloud with sharp feature,at the same time,this algorithm can precisely extract feature points from point cloud models and emphasizing the sharp features of the models.