由粗到细的颅骨点云模型配准方法  

Registration Method from Coarse to Fine of Skull Point Cloud Model

作　　者：赵夫群[1] ZHAO Fuqun(School of Education Science,Xianyang Normal University,Xianyang 712000,China)

机构地区：[1]咸阳师范学院教育科学学院,陕西咸阳712000

出　　处：《测绘通报》2018年第12期26-29,35,共5页Bulletin of Surveying and Mapping

基　　金：国家自然科学基金(61731015; 61672013);咸阳师范学院青年骨干教师培养项目(XSYGG201621)

摘　　要：颅骨配准是颅面复原的一个重要步骤,其配准精度对复原结果有着重要的影响。为了提高颅骨配准的精度,并解决分辨率差异较大颅骨的配准问题,本文提出了一种由粗到细的三维颅骨点云模型配准方法。首先,采用基于神经网络(NN)的点云配准算法实现颅骨粗配准;然后,通过添加尺度因子和模拟退火系数以改进迭代最近点(ICP)算法并实现颅骨细配准,大大提高了颅骨细配准的精度和速度,从而实现颅骨的精确配准。将一个未知颅骨与颅骨库中的300个颅骨进行配准试验,结果表明,基于NN的点云配准算法可以实现颅骨的初始配准,并且改进ICP算法在细配准阶段的配准精度和速度,比ICP算法分别提高了约30%和50%。因此,提出的由粗到细的点云配准算法是一种有效的颅骨配准算法,可以实现三维颅骨点云模型的精确配准。Skull registration is an important step of craniofacial restoration,the accuracy of which plays a key role to the recovered results.To improve the registration accuracy of skulls and solve the registration problem of different solutions,a 3D skull point cloud registration method from coarse to fine is proposed in the paper.Firstly,coarse registration of skulls is completed using the point cloud registration method based on neural network (NN).Then the iterative closest point (ICP)is improved by integrating scale factor and simulated annealing coefficient into it and used to complete skull fine registration.The improved ICP algorithm could improve the accuracy and speed of fine registration and achieve the final registration of skulls with high accuracy.An unknown skull is registered with 300 skulls in database in the experiment,the results show that the NN-based point cloud registration method could align the skulls coarsely,and the registration accuracy and convergence rate of improved ICP algorithm increase by about 30% and 50% respectively compared with ICP algorithm.So the proposed skull registration method from coarse to fine is an effective skull registration method which could complete accurate registration of 3D skull model.

关 键 词：颅骨配准 神经网络 迭代最近点 尺度因子 模拟退火 

分 类 号：P234.4[天文地球—摄影测量与遥感]