关键词: 信息相似度, 有效邻域, 自适应, 各向异性高斯核, 点云去噪

Abstract: To solve the problem of excessive smoothing and local distortion caused by traditional point cloud denoising algorithm,a feature preserving and anisotropic filtering algorithm was proposed based on anisotropic Gaussian kernel.Informational similarity function was constructed according to the projection of neighborhood point in the tangent plane and the normal of sampling point,and effective neighborhood of sampling points was defined according to the information similarity function.Distribution properties of eigenvalues and eigenvectors of surface points,curve points and corner points were researched by using principal component analysis theory.On this basis,parametric adaptive and anisotropic Gauss kernel function was constructed by taking pseudo inverse matrix of covariance matrix as the bandwidth matrix,and combined it with the bilateral filtering algorithm for scattered point cloud denoising.The experimental results showed that the algorithm could adjust filter main direction and attenuation velocity of each principal direction adaptively according to the local distribution properties,the algorithm could denoise the point cloud well and preserve original sharp feature at the same time.

Key words: informational similarity, effective neighbor, adaptive, anisotropic Gaussian kernel, point cloud denoising