颅骨配准是颅面复原的重要步骤之一, 其配准精度和效率对复原结果有着重要的影响。为了提高颅骨点云模型的配准精度和效率, 本文提出了一种层次优化的颅骨点云配准方法。将颅骨配准分为粗配准和细配准两个过程。首先对颅骨点云模型进行去噪、简化和归一化等预处理; 然后对颅骨点云模型提取特征点并计算其特征序列, 根据特征序列进行约束寻找初始对应点对, 并采用k-means算法剔除误匹配点, 实现颅骨粗配准; 最后通过加入几何特征约束的改进迭代最近点(ICP)算法实现颅骨细配准, 从而达到颅骨精确配准的目的。本文分别对粗配准、细配准和先粗再细完整配准过程进行实验, 结果表明: 粗配准过程, 与未优化的粗配准算法相比, 本文优化后的粗配准算法的配准精度提高了约35%, 算法耗时增加了约6%; 细配准过程, 与ICP算法相比, 本文改进ICP算法的配准精度和收敛速度分别提高了约20%和43%, 算法耗时减少了约47%; 先粗再细的完整配准过程, 本文算法的配准精度和收敛速度都要优于其他两种方法。证明了本文方法是一种有效的颅骨点云配准算法, 可以实现颅骨点云的精确配准。

Skull registration is one of the most important steps in craniofacial reconstruction. Its accuracy and efficiency have amajor impact on craniofacial reconstruction results. To improve the accuracy and efficiency of skull point cloud registration, this study proposed a hierarchical optimization method for skull point cloud registration. We divided skull registration into two processes, coarse and fine. First, the skull cloud model was denoised, simplified, and normalized. Then, the feature points were extracted from the skull point cloud model and their feature sequences were calculated. The initial corresponding point pairs were constrained based on the feature sequence, and the algorithm was used to eliminate the mismatched points to achieve coarse registration of the skull. Finally, an improved Iterative Closest Point (ICP) algorithm with geometric feature constraints was used to achieve fine skull registration to achieve the goal of accurate skull registration. In this study, experiments on rough, fine, and first coarse and then fine registration were conducted. Results show that in the coarse registration process, the registration accuracy of the optimized coarse registration algorithm is improved by approximately 35%, and the algorithm time consumption is increased by approximately 6% as compared with the unoptimized coarse registration algorithm. In the fine registration process, the registration accuracy and convergence speed of the improved ICP algorithm are improved by approximately 20% and 43%, respectively, and the time consumption of the algorithm is reduced by approximately 47% as compared with the ICP algorithm. For the complete registration process, the registration accuracy and convergence speed of the algorithm are better than those of the other two methods. Therefore, this method is an effective skull point cloud registration algorithm that can achieve accurate registration of a skull point cloud.