颅骨配准是颅面复原过程中的重要步骤之一,颅骨配准的精度直接影响着颅面复原结果的好坏。为了提高颅骨点云模型的配准精度和收敛速度,提出一种基于分层优化策略的颅骨点云配准算法,将配准过程分为粗配准和细配准两个过程,分别采用不同的优化策略进行优化。首先基于点的邻域提取几何特征,从而得到由平均曲率、高斯曲率、法向量夹角和主曲率构成的特征向量;进一步通过距离函数计算特征相似性来建立匹配点对,并采用k-means算法剔除误匹配点对;然后使用四元数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k-维(k-d)树和加入几何特征约束对迭代最近点(ICP)算法进行改进,使用改进的ICP算法实现颅骨的精确配准。实验结果表明:粗配准过程采用k-means算法剔除误匹配点对的优化策略和细配准过程加入k-d树与几何特征约束的优化策略都是有效的。与ICP算法相比,本文算法的匹配率和配准精度分别提高了约17%和51%,算法耗时减少了约31%。与其他经典配准算法和改进的ICP算法相比,本文算法的配准效率是最优的。为了验证本文算法的普适性,还采用兵马俑碎片数据进行验证,本文算法也取得了较好的效果和最优的性能。因此,本文算法是一种有效的颅骨点云配准方法。

Skull registration is one of the important steps in the process of craniofacial restoration. The accuracy of skull registration directly affects the outcome of craniofacial restoration. In order to improve the registration accuracy and convergence speed of skull point cloud model, a registration algorithm based on the hierarchical optimization strategy is proposed. The registration process is divided into two processes, coarse registration and fine registration. The different optimization strategies are used for optimization. Firstly, the geometric features are extracted based on the neighborhood of points, and then the eigenvectors consisting of mean curvature, Gauss curvature, normal vector angle, and principal curvature are obtained. Further, the feature similarity is calculated by distance function to establish matching point pairs, and k-means algorithm is used to eliminate the mismatching point pairs. Then the quaternion method is used to calculate the rigid body transformation relationship between the skull point clouds to achieve skull coarse registration. Finally, the improved iterative closest point (ICP) algorithm is improved by the introducing k-d tree and geometric feature constraints. The improved ICP algorithm is used to achieve accurate skull registration. The experimental results show that it is effective to use the k-means algorithm to eliminate the mismatched point pair optimization strategy. It is also effective to add the k-d tree and geometric feature constraint optimization strategy to the fine registration process. Compared with ICP algorithm, the matching rate and registration accuracy of this algorithm are improved by 17% and 51%, respectively, and the time-consuming is reduced by 31%. Compared with other classical registration algorithms and improved ICP algorithm, the efficiency of the proposed algorithm is the best. In order to verify the universality of the algorithm, the terra cotta warriors fragment data is also used to verify, and the proposed algorithm achieves good results and optimal performance. Therefore, the proposed algorithm is an effective point cloud registration method.