The Research And Implementation Of High-Precision Surface 3D Reconstruction Based On Point Cloud

3D reconstruction technology has always been a hot research field in graphics image processing and 3D visual effects optimization.Faced with the development of modem industrial and manufacturing design and production,3D surface reconstruction can reduce the design cost and design cycle during production,and can be applied to many other related fields.It has important practical value for the research of 3D surface reconstruction.Affected by the current point cloud data collection methods,the number of point cloud data in the point cloud dataset is often massive data.Although these data can reflect the real surface features of the device,there are still a lot of noise points,how to efficiently handle massive high precision.Point cloud data,rapid reconstruction of the surface is a problem worth studying.Aiming at the surface reconstruction problem of massive point cloud data model acquired by laser scanning,this paper fully studies the spatial geometry and edge distribution of point cloud dataset from the point of view of point cloud data collection and scanning technology.In this paper,three common surface reconstruction algorithms are studied in detail,namely Poisson algorithm,Marching Cubes algorithm and Greedy Proj ection Triangulation algorithm.First,according to the characteristics of point cloud data and the existing research results of point cloud data preprocessing,a RANSAC algorithm based on voxel segmentation is proposed and applied to 3D surface reconstruction.The effects of the RANSAC algorithm based on voxel segmentation and the common algorithm of 3D reconstruction of model surface are compared by experiments.The experimental results of qualitative and quantitative results show that compared with the reconstruction algorithm based on the classical RANSAC algorithm,the proposed algorithm can effectively reduce the surface reconstruction time of 47.51%,and the model error after processing is only one thousandth compared with the original model.One of the points meets the accuracy requirements of massive 3D simulation of point cloud.Then in this paper,MFC and OpenGL 3D graphics library functions are used to build a 3D visualization platform for data point cloud model.The visualization operation platform software can be used by researchers of model reverse engineering.The visual operation platform can meet the user's requirements for 3D human-computer interaction of the model,including multi-view observation,far-distance observation,multi-angle observation,point cloud model movement,space coordinate system and grid line drawing.The model also has point-to-face statistical analysis of the point cloud model,central coordinate calculation,and so on.Compared with the three common surface reconstruction algorithms,by analyzing the characteristics of the original point cloud data and the output data set,it is shown that the proposed method can improve the reconstruction speed of 3D model while retaining effective feature information and high precision.The built 3D visualization platform features visual 3D human-computer interaction settings,simplified intuitive interface,and the ability to display and manipulate in real time.The platform can improve the efficiency of 3D point cloud model scene rendering,and can meet the requirements of reconstruction accuracy of 10 ?m.