点云模型法矢调整优化算法

Optimal algorithm for normal adjustment of point clouds

点云中存在奇异情况时,采用最小生成树法进行法矢调整会出现错误,而采用曲面重建方法运算效率又较低,为此提出一种点云模型法矢调整的优化算法。算法分别处理薄壁特征、垂直法向和相邻曲面3种奇异情况。对薄壁特征,算法提取特征点并在该处强制进行法矢取反;对垂直法向,算法通过扩大邻域搜索范围来获得法矢变化趋势;对相邻曲面,算法在K邻域中剔除歧义邻域点,避免在最小生成树中生成错误边。实验结果表明,该算法在点云中存在奇异情况时能够进行正确的法矢调整,并且相较于曲面重建方法具有较高的效率。

When abnormal conditions occur in point clouds, the normal adjustment may have erroneous results when using the minimum spanning tree algorithm, while the efficiency is low when using the surface reconstruction algorithm. In order to solve this problem, an optimal algorithm for normal adjustment of point cloud is proposed. It deals with three abnormal conditions separately. For the thin feature condition, it exacts feature points and reverses orientations compulsively. For the perpendicular normal condition, the neighboring region is expanded to get the tendency of the normal. For close-by surfaces condition, ambiguous neighbors are removed from the K-nearest neighbors to avoid creating an erroneous minimum spanning tree edge. Experiments show that the algorithm can adjust the normals correetty even when such abnormal conditions exist. Compared with surface reconstruction algorithm, the algorithm can adjust the normals more efficiently.