Various existing methods cannot reliably estimate the normal vectors for a point cloud model to smooth sharp features during point cloud processing. To address this problem

we developed a novel method based on Gaussian mapping to estimate the normal vectors of a scattered point cloud with sharp features. First

the normal vectors and feature points were roughly estimated by principal component analysis method. The feature points and their neighborhood points were mapped into a Gaussian sphere. Then

the K-means clustering algorithm was employed to segment data on the Gaussian sphere to several sub-clusters. Normal vector of a point is accurately estimated with the anisotropy neighborhood points that corresponded to the optimal sub-cluster to fit surface. Last

the effectiveness of the proposed method was validated by measuring the average deviation of the estimated normal vector from the standard normal vector. The estimated normal vectors were used in surface reconstruction to verify the feature-preserving property of the proposed method. Experimental results demonstrated that the least average deviation is close to zero. The method can accurately estimate the normal for noisy data. The reconstructed model maintains original geometry when the normal is used as input for the surface reconstruction algorithm. Compared with other normal estimation methods

the proposed method can more accurately estimate the normal vectors of points. The proposed method can accurately estimate the normal vector of a point model with sharp features. The method also exhibits high adaptability and robustness for point clouds with noise.