为了提高颅骨点云模型的配准精度和收敛速度,提出一种基于曲率图的颅骨点云配准方法。首先对颅骨点云提取以特征点为中心并且包含其相邻点的三维形状块,将所有点投影到二维平面上;将投影点量化到二维支撑区域的相应单元中,并将其加权曲率编码为曲率分布图来构造特征点的区域曲率图描述符;然后基于区域曲率图描述符匹配具有相似局部形状的点来建立匹配点对,采用奇异值分解方法计算颅骨点云间的刚体变换关系,实现颅骨粗配准;最后通过引入动态迭代系数对迭代最近点(ICP)算法进行改进,使用改进的ICP算法实现颅骨的细配准。实验结果表明,所提粗配准方法是一种有效的初始配准方法。与ICP算法相比,改进的ICP算法在配准精度和收敛速度上分别提高了约11%和37%,配准耗时降低了约34%。为了验证所提方法的普适性,还采用兔子点云模型进行验证,结果显示改进的ICP算法的配准效果优于ICP算法。

This paper presents a new skull point cloud registration method based on curvature maps to improve the registration accuracy and convergence speed of the skull point cloud model. First, a three-dimensional shape block centered on the feature points and containing its adjacent points is extracted from the skull point cloud, and all the points are projected onto the two-dimensional plane. Furthermore, the projection points are quantized into the corresponding units in the two-dimensional supporting area, and the weighted curvature is encoded as curvature distribution images to construct the region curvature map descriptors of the feature points. Then, matching point pairs are established by matching points with similar local shapes based on regional curvature map descriptors, and the rigid body transformation relationship between skull point clouds is calculated using the singular value decomposition method to realize skull coarse registration. Finally, the iterative closest point (ICP) algorithm is improved by introducing dynamic iteration coefficients and used to achieve fine skull registration. The experiment results demonstrate that the proposed rough registration method is an effective initial registration method. Compared with the original ICP algorithm, the improved ICP algorithm increases the registration accuracy and convergence speed by approximately 11% and 37%, respectively, and reduces the time-consumption by approximately 34%. The bunny point cloud model is used to verify the generalization ability of the proposed method. The results demonstrate that the registration effects of the improved ICP algorithm are better than those of the original ICP algorithm.