Three-dimensional scattered point cloud smoothing denoising method of anisotropic diffusion filtering

Abstract

The invention discloses a three-dimensional scattered point cloud smoothing denoising method of anisotropic diffusion filtering. A tensor matrix structure tensor matrix is obtained by tensor voting ona sampling point and effective neighboring points thereof, eigenvalues and eigenvectors are solved, according to the eigenvalues and eigenvectors of the structure tensor matrix, local characteristicsof the sampling point are analyzed, the eigenvalues of a diffusion tensor matrix are designed according to different geometric feature information of the sampling point, diffusion rates are designedaccording to the different geometric feature information so that the diffusion rates in different principal feature directions are different, a modified diffusion tensor matrix is reconstructed, finally, the reconstructed diffusion tensor is substituted into a three-dimensional diffusion anisotropic filtering equation for differential solution, and after a certain number of iterations, a filteringfactor is obtained for smoothing noise. The method can effectively remove noise of the scattered point cloud and maintain feature information of an original model at the same time, thereby avoiding excessive smoothing and local distortion.

Description

technical field [0001] The invention relates to the reverse engineering field of computer vision technology and three-dimensional reconstruction, in particular to a method for smoothing and denoising three-dimensional scattered point clouds by anisotropic diffusion filtering. Background technique [0002] The acquisition of 3D point cloud data of objects is an important part of reverse engineering. However, due to the defects of the measurement equipment itself and the influence of the measurement environment, the measured 3D point cloud data inevitably has noise. Therefore, smoothing and denoising processing of 3D point cloud data has become one of the hot issues in the field of reverse engineering research. [0003] With the continuous development of computers and 3D digitization, 3D point models are also widely used in 3D printing, virtual reality and computer modeling and other fields. However, the point cloud data obtained by the 3D scanner contains noise, and the tra...

AI Technical Summary

Problems solved by technology

However, the point cloud data obtained by the 3D scanner contains noise. The traditional denoising method will easily lead to the loss of model feature details when removing noise. Therefore, a denoising method for point cloud features is particularly important.

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