Research On Feature Constraint Simplification And 3D Reconstruction Technology Based On Point Cloud Data

The surface morphology reconstruction technology based on point cloud data is one of the key technologies of 3D information processing,which has been widely used in reverse engineering,digital manufacturing and other fields.With the development of 3D scanning technology,the large amount of original point cloud data and the existence of a large number of redundancies can reduce the efficiency of point cloud data utilization and bring inconvenience to the processing and application of point cloud data.In order to quickly and efficiently reconstruct the surface morphology of 3D point clouds,this thesis proposes a 3D Poisson reconstruction algorithm based on the feature-constrained streamlining of point cloud data to address the problems of over-simplification,missing feature points and low reconstruction efficiency in traditional point cloud processing algorithms,and verifies the effectiveness of this algorithm through data set experiments and practical application experiments.The main research contents of this thesis are as follows:Firstly,by introducing the characteristics of discrete disorder in space of 3D point cloud and the analysis and comparison of point cloud data indexing methods,a fast query of 3D point cloud data neighborhood information is realized based on the point cloud KD tree as the data indexing structure,which provides data search method support for the feature parameter calculation of point cloud data.At the same time,the Voronoi diagram and Delaunay triangular dissection methods of point cloud data are analyzed,and different dissection processing methods are proposed for point cloud model regions.Secondly,to address the problem of over-simplification of point cloud data in the simplification process of traditional point cloud data simplification algorithms,this thesis proposes an improved feature-constrained point cloud data simplification algorithm from the perspective of reducing the feature loss in the simplification process of point cloud data.The point cloud model is divided into feature regions and non-feature regions,where the feature regions are jointly constrained by local features extracted from the geometric curvature description of the point cloud and the overall features extracted based on the normal vector angle description.At the same time,in order to ensure the subsequent good surface reconstruction effect,the non-featured point cloud region is simplified by the envelope box voxel simplification method,and the two regions are combined as the final simplification result of this thesis,so as to realize the fast simplification processing of the point cloud without losing the point cloud features and obtain the point cloud data models with different simplification rates.Thirdly,based on the traditional Poisson reconstruction algorithm,an implicit3 D reconstruction method based on feature constraints is proposed by combining the feature constraint to refine the point cloud and normal vector adjustment.And the implicit surface function is used to solve the point cloud vector field,and the moving cube algorithm is used to construct the model equivalent surface to realize the 3D surface reconstruction.Experiments on the dataset show that the method in this thesis can achieve surface reconstruction for point cloud data and obtain a complete reconstructed data model;it can construct a point cloud surface model with less number of triangular facets without changing the structural features of the model,and effectively reduce the reconstruction time of 3D point cloud data.And by designing a standard point cloud cube data model,the accuracy of the reconstruction method in this thesis is verified.Finally,the 3D reconstruction visualization experimental platform built was able to successfully apply the point cloud processing algorithm proposed in this thesis to the actual collected point cloud data.And the data visualization of thread point clouds of different sizes was realized in the upper computer terminal to obtain a complete digital virtual thread model.At the same time,the VTK-based local magnification function was able to digitally detect small defects on the thread surface,thus demonstrating the effectiveness of the feature constraint refinement algorithm and 3D reconstruction algorithm proposed in this thesis in practical engineering applications.In addition,five common types of point cloud data were selected for the feature constraint refinement and 3D reconstruction experiments,and the experimental results further verified the generality and effectiveness of the proposed method.