Abstract
Abstract. In point cloud data processing, smooth sampling and surface reconstruction are important aspects of point cloud data processing. In view of the current point cloud sampling method, the point cloud distribution is not uniform, the point cloud feature information is incomplete, and the reconstructed model surface is not smooth. This paper proposes a method of smoothing sampling processing and surface reconstruction using point cloud using moving least squares method. This paper first introduces the traditional moving least squares method in detail, and then proposes an improved moving least squares method for point cloud smooth sampling and surface reconstruction. In this paper, the algorithm is designed for the proposed theory, combined with C++ and point cloud library PCL programming, using voxel grid sampling and uniform sampling and moving least squares smooth sampling comparison, after sampling, using greedy triangulation algorithm surface reconstruction. The experimental results show that the improved moving least squares method performs point cloud smooth sampling more uniformly than the voxel grid sampling and the feature information is more prominent. The surface reconstructed by the moving least squares method is smooth, the surface reconstructed by the voxel grid sampling and the uniformly sampled data surface is rough, and the surface has a rough triangular surface. Point cloud smooth sampling and surface reconstruction based on moving least squares method can better maintain point cloud feature information and smooth model smoothness. The superiority and effectiveness of the method are demonstrated, which provides a reference for the subsequent study of point cloud sampling and surface reconstruction.
Highlights
With the rapid development of modern scanning technology, 3D reconstruction technology has been widely used in various industry fields
If these raw data are directly used for surface reconstruction, the reconstructed surface will be not smooth or vulnerable, and the desired surface model will not be obtained
Literature (Bernard et al, 2017) proposed a statistical shape model using the associated point distribution model, but due to the heteroscedasticity of the point cloud data, the surface generated by the surface reconstruction method using only the probabilistic model will have a certain deviation
Summary
With the rapid development of modern scanning technology, 3D reconstruction technology has been widely used in various industry fields. The feature area and the flat area are divided based on the curvature value, and the curvature sampling and uniform sampling processing are used respectively, achieve the effect of reducing point clouds and retaining the details of the model. This method is more difficult to determine the division of the average curvature of each data point with the specified average curvature value, and is not suitable for flat area point clouds. This paper proposes a point cloud smooth sampling and surface reconstruction based on moving least squares method
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