Anisotropic neighborhood searching for point cloud with sharp feature
The k-nearest neighborhoods (kNN) of feature points of complex surface model are usually isotropic, which may lead to sharp feature blurring during data processing, such as noise removal and surface reconstruction. To address this issue, a new method was proposed to search the anisotropic neighborhood for point cloud with sharp feature. Constructing KD tree and calculating kNN for point cloud data, the principal component analysis method was employed to detect feature points and estimate normal vectors of points. Moreover, improved bilateral normal filter was used to refine the normal vector of feature point to obtain more accurate normal vector. The isotropic kNN of feature point were segmented by mapping the kNN into Gaussian sphere to form different data-clusters, with the hierarchical clustering method used to separate the data in Gaussian sphere into different clusters. The optimal anisotropic neighborhoods of feature point corresponded to the cluster data with the maximum point number. To validate the effectiveness of our method, the anisotropic neighbors are applied to point data processing, such as normal estimation and point cloud denoising. Experimental results demonstrate that the proposed algorithm in the work is more time-consuming, but provides a more accurate result for point cloud processing by comparing with other kNN searching methods. The anisotropic neighborhood searched by our method can be used to normal estimation, denoising, surface fitting and reconstruction et al. for point cloud with sharp feature, and our method can provide more accurate result comparing with isotropic neighborhood.
