Research On Simplification Algorithm Of 3D Laser Scanning Measurement Point Cloud

With the development of 3D laser scanning technology,3D laser scanning technology has been widely used in virtual reality,precision manufacturing,surveying and mapping and other fields.Fast and accurate modeling of object shape and measurement of object size have become the key problems of 3D laser scanning technology.When using3 D laser scanner to measure,it is necessary not only to preserve the main features of the original model but also to control the measurement accuracy.However,while describing the model features in detail,the massive point cloud data collected by laser scanner will affect the processing efficiency of point cloud data,so it is necessary to carry out surface reconstruction,surface parameterization,simplification and other processing of point cloud data.In this paper,surface parameterization and point cloud data simplification in three-dimensional laser scanning point cloud measurement are studied.The main research work is as follows:Firstly,in view of the problems of information loss,surface deformation and high computational complexity in the process of point cloud data parameterization,this paper proposes a Ricci flow algorithm based on quasi-Newton method to parameterize three-dimensional point cloud mesh data and obtain two-dimensional point cloud data coordinates of model surface.In the calculation process of Ricci flow algorithm,the target Gaussian curvature of each inner point and boundary point in the three-dimensional curved surface mesh is set first.According to the set target Gaussian curvature,the quasi-Newton iteration method is used to optimize the energy of Ricci discrete entropy step by step.By embedding a three-dimensional surface into a two-dimensional plane,the parameter domain coordinates of each point on the three-dimensional surface mesh are obtained,and the three-dimensional geometric information of the model surface is preserved.Secondly,in order to solve the problem that the boundary of the model is not smooth and the curvature changes greatly when triangular mesh is reduced,the quadratic error measure point cloud reduction algorithm based on Ricci flow parameterization is proposed.Based on the parameter coordinates of each vertex in the point cloud mesh,the algorithm extends the mesh vertex dimension and calculates the quadratic error cost function after the extended dimension.On this basis,the total cost function of edge folding is optimized by using the weighted criterion of the opposite edge length and angle constraint of vertices.All generation values are sorted according to the size of edge folding cost,and the point pairs with low cost are selected as the point pairs with priority to be deleted,and the positions of new vertices are calculated.The process is repeated continuously until the reduction rate is achieved,and the simplified point cloud mesh model is finally obtained.Finally,a 3D point cloud data measurement experimental system is built in the laboratory,and the point cloud mesh simplification algorithm proposed in this paper is applied to simplify the point cloud data measured in the laboratory.In this paper,the effectiveness of the proposed point cloud simplification algorithm is verified by several indexes,such as mesh generation quality,measurement accuracy and simplification degree,through the point cloud simplification comparison experiments of multiple models.