Study on Smooth Denoising of 3D Scattered Point Clouds with Anisotropic Diffusion Filtering

To solve the distortion problem of model feature information during the denoising process based on the traditional point cloud denoising algorithm,a 3D scattered point cloud smoothing denoising algorithm was proposed based on anisotropic diffusion filtering.Firstly,the tensor matrix of the sampling point was obtained by tensor voting algorithm,and the eigenvalues and eigenvectors of the tensor matrix were solved;secondly,to adjust adaptively the diffusion rate of the different characteristic direction,the eigenvalues of diffusion tensor were designed based on the geometric characteristic of sampling point;finally,a point cloud filtering model was constructed for denoising by combining the reconstructed diffusion tensor with the 3D anisotropic diffusion filtering equation.The experimental results of different point cloud models with noise showed that the feature information of the original model was preserved effectively and the noise from the point cloud was removed.Therefore,the excessive smoothing of the model was avoided.