基于局部结构约束的点云法向估计算法
Point Cloud Normal Estimation Algorithm Based on Local Structure Constraints

法向量作为点云不可或缺的属性之一，在诸多算法中具有重要作用，因此法向估计一直是处理点云的一个重要任务。然而，对于尖锐特征点，其邻域是两个表面或者多个表面的交汇处，这会增加法向估计的难度。在处理尖锐特征点时，现有的一些算法仅仅考虑了点和平面之间的距离属性，在法向估计时仍存在一些缺陷。为了更好地改善上述问题，Wang等人通过加权平面拟合的方法，不仅考虑点和平面之间的距离属性，而且加入了法向属性，该方法提高了法向估计的准确性。然而该方法并未考虑点和平面之间的法向属性。因此，本文在该方法的框架之上，考虑了点和拟合平面的法向差。实验结果表明，本文算法在法向估计的准确性上相比现有的算法有所提高。
As one of the indispensable attributes of point cloud, normal vector plays an important role in many algorithms, so normal estimation has always been an important task in processing point cloud. However, for sharp feature points, their neighborhood is the intersection of two or more surfaces, which increases the difficulty of normal estimation. When processing sharp feature points, some existing algorithms only consider the distance attribute between the point and the plane, and there are still some defects in normal estimation. In order to better improve the above problems, Wang et al. not only consider the distance attribute between the point and the plane, but also add the normal attribute by the weighted plane fitting method, which improves the accuracy of normal estimation. However, this method does not consider the normal attribute between the point and the plane. Therefore, this paper considers the normal difference between the point and the fitting plane on the framework of this method. Experimental results show that the accuracy of normal estimation of the proposed algorithm is improved compared with the existing algorithms.