Research On Hole Recognition And Repair Technology Of Point Cloud Model In Reverse Engineering

In the information age,with the application of three-dimensional model expression in various fields,point cloud model,as a new geometric model,has received more and more attention.However,when acquiring the spatial coordinates of points on the surface of the target object by measuring equipment,the lack of data caused by scanning equipment and the object itself causes the hole in the point cloud model.In order to maintain the integrity of point cloud data without affecting the subsequent operations such as surface reconstruction and entity reconstruction,it is necessary to analyze and repair the data defects of the obtained point cloud model.In this paper,after studying the existing methods of hole identification and repairing,the problem is studied and new progress has been made.Firstly,the definition of hole in point cloud model is not clear,and the difference of measuring equipment and objects makes the data missing types of different point cloud models vary greatly.This paper analyses the causes of hole formation from common measuring methods and pre-processing process,summarizes different point cloud models,systematically summarizes and classifies the hole types of point cloud model,and supplements the current research on point cloud data missing.The short board of the point cloud model paves the way for the hole identification and repair of the follow-up point cloud model.Secondly,it is not effective to identify holes only by using point feature attributes in scattered point cloud data without topological structure,and most methods use two-dimensional projection to determine the location of holes,but there are some deficiencies in three-dimensional identification methods.In this paper,a voxel-based voxel recognition method for point cloud model is proposed.The voxel is processed by inputting the whole point cloud model.The voxel boundary voxels of closed point cloud are extracted by using the connection relationship between voxel units.The boundary voxels are used to determine the boundary points in the initial point cloud model.Then RPCL-FCM clustering algorithm is applied to the division of the boundary points of the hole cloud model.Hole boundaries are divided,unless the hole data points cluster is eliminated,and the nearest point search method is used to connect the boundary points to form boundary lines.Examples show that the method can effectively identify and extract holes in point cloud model in three-dimensional space,and reduce the computational cost of repeated mapping and transformation between two-dimensional and three-dimensional data.Then,aiming at different types of hole repair problems,different point cloud models have different types of holes in the process of processing.The existing algorithms focus on single holes to improve the repair accuracy and efficiency,but they are not applied to different holes in different models.In this paper,BP neural network and optimized BP neural network are applied to different types of hole repairing in different models.The deviations of different algorithms in different types of hole repairing are analyzed,and the conclusions are drawn.On this basis,the repairing strategies for different types of holes are summarized,which provides a basis for the repairing of similar natural holes.Finally,in order to verify the practicability and significance of the research method,and to promote the process of the project,the paper applied the research method to deal with the new test pieces,and realized the hole identification and hole repair operation of the point cloud model of the vehicle suspension control arm.The repair accuracy and error analysis showed that the effect was good.