尖锐特征曲面散乱点云法向估计

针对现有算法对尖锐特征曲面点云法矢估计不准确,点云处理时容易丢失曲面细节特征等问题,提出一种尖锐特征曲面散乱点云法向估计法。该方法用主成分分析法粗估计点云法向;然后,根据各邻域点的空间欧氏距离和法向距离对各邻域法向加权,用加权邻域法向之和来更新当前点的法向;最后,测试估计法向与标准法向的误差,评价估计法矢的准确性,并且将估计的法向应用到点云数据处理中来比较特征保留效果。实验结果表明:本文方法能够准确地估计尖锐特征曲面的法向,最小误差接近0。另外,该方法对噪声有较好的鲁棒性,点云处理时能保留曲面的尖锐特征。相比于其他特征曲面法向估计法,所提出的方法估计的法向误差更小、速度更快、耗时更少。

Normal estimation of scattered point cloud with sharp feature

A novel method was proposed to estimate the normal for a scattered point cloud with sharp features to overcome the shortcomings that existing methods are unable to reliably estimate the normal of point cloud model and lead to the smoothed sharp features. With proposed method, the normal of point cloud was estimated with principal component analysis method. Then, different values were weighted on neighborhood normals according to spatial distance and normal distance of current points of the neighborhood, and the revised or current normals were updated by the sum of weighted neighborhood normal. Finally, the average deviation between standard normal and estimated normal was measured and the accuracy of estimated normal was evaluated. The estimated normal was applied to point cloud processing to verify the feature-preserving property. The proposed method was validated. The results demonstrate that proposed method accurately estimates the normal for data with noise and the least average deviation is close to 0. Moreover, the method has good robustness to the niose, and it keeps the original geometry well when the normal is used as input of the point cloud processing. Comparing with other sharp feature preserving normal estimation methods, the proposed method shows smaller average deviation, higher processing speeds and less computation time.