中文摘要：

目的 针对特征曲面点云法矢估计不准确，点云处理时容易丢失曲面的细节特征等问题，提出基于高斯映射的特征曲面散乱点云法向估计法。方法 首先，用主成分分析法粗略地估算点云法向和特征点；其次，将特征点的各向同性邻域映射到高斯球，用K均值聚类法对高斯球上的数据分割成多个子集，以最优子集对应的各向异性邻域拟合曲面来精确估算特征点的法向量；最后，通过测试估计法向与标准法向的误差来评价估计法矢的准确性，并且将估计的法向应用到点云曲面重建中来比较特征保留效果。结果 本文方法估计的法向最小误差接近0，对噪声有较好的鲁棒性，重建的曲面能保留曲面的尖锐特征，相比于其他法向估计法，所提出的方法估计的法向更准确。结论 本文方法能够比较准确的估算尖锐特征曲面法向量，对噪声鲁棒性强，具有较高的适用性。

英文摘要：

Objective 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.Methods 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.Results 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.Conclusion 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.