Restore the appearance of an unknownskull during a craniofacial reconstruction procedure based on knowledge

the most similar skull should be retrieved from the database. Then

the reference face can be found according to the most similar skull. The process of searching for the most similar skull is called skull registration. In the skull registration problem

accuracy and efficiency are the two important performance targets that cannot be disregraded. A novel method for skull registration based on feature region extraction and the modified iterative closest point (ICP) algorithm is proposed in this study. The novel method is called the hierarchical skull registration method with an iterative factor. First

the skull model is denoised

simplified

and normalized. Then

the convexity or non-convexity of each point in the point cloud model is determined using the method based on an integral invariant. The surface of the skull can be divided into concave or convex feature regions via the -means clustering method. The similarity of the concave or convex regions between two different skulls is subsequently calculated by comparing their principal components and areas. Optimal matching is conducted through exhaustive search. The optimal 3D transform for each potential pair of matched feature regions approximately aligns the skull surface. Lastly

the novel ICP algorithm with an iteration factor is applied to achieve fine registration. The presented method is applied to the skull registration of a Terracotta Army model and a public data set. The registration time of the classical ICP algorithm is 6.23 s

7.61 s

and 4.17 s

whereas that of the improved ICP algorithm is 3.02 s

3.23 s

2.83 s

respectively. Registration efficiency is doubled and accuracyis also significantly improved. However

the iteration factor varies for different data sets. Experimental results show that the proposed algorithm can achieve better registration accuracy and higher iterative convergence speed in the fine registration stage.The entire process is completed without human intervention and demonstrates certain adaptability. It can be used for similar 3D model registrations.