Hierarchical point cloud denoising algorithm

Abstract

The initial point cloud model acquired by 3D laser scanning equipment contains more noise points that is not good for the later point cloud processing. Therefore, the noise needs to be deleted. A hierarchical point cloud coarse-to-fine denoising algorithm was proposed for effective retention of the sharp geometric features of the point cloud. The tensor voting matrix of the points and their neighbors was constructed. In addition, the diffusion tensor was constructed by calculating the eigenvalues and eigenvectors of the matrix. The diffusion tensor-based anisotropic diffusion equation was applied for cyclic filtering, to realize the initial coarse denoising of the point cloud. Further, the curvature feature of the point cloud was calculated post-filtering. To achieve fine denoising, the noise points in the point cloud were further deleted according to the curvature value. Finally, the point cloud entropy was calculated for quantitative evaluation of the denoising algorithm. The experimental results demonstrate that the proposed point cloud denoising algorithm exhibited a smaller denoising error, higher entropy value, and high execution efficiency. The proposed hierarchical point cloud denoising algorithm can quickly and accurately delete noise points, while retaining sharper geometric features of the point cloud. Therefore, it is an effective point cloud denoising algorithm.