层次优化的颅骨点云配准

杨稳; 周明全; 耿国华; 刘晓宁; 李康; 张海波

doi:10.3788/OPE.20192712.2730

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层次优化的颅骨点云配准

信息科学 | 更新时间：2020-07-09

- 层次优化的颅骨点云配准
- Hierarchical optimization of skull point cloud registration
- 光学精密工程 2019年27卷第12期页码：2730-2739
- 作者机构：
  
  1.西北大学信息科学与技术学院, 陕西西安 710127
  2.北京师范大学信息科学与技术学院, 北京 100875
- 作者简介：
  
  [ "杨稳(1993-)，男，陕西安康人，博士研究生，主要从事机器学习与模式识别方面的研究。E-mail：yw@stumail.nwu.edu.cn" ]
  [ "周明全(1954-)，男，陕西西安人，教授，博士生导师，1976年、1988年于西北大学分别获得学士、硕士学位，主要从事虚拟现实、可视化技术、智能信息处理信息处理等领域的基础理论及其应用工程创新的研究。E-mail：mqzhou@bnu.edu.cn" ]
- 基金信息：
  
  国家自然科学基金面上项目(61673319);国家自然科学基金资助项目(61731015);国家重点研发项目(2017YFB1402103);陕西省产业创新链项目(2016TZC-G-3-5);陕西省自然科学基础研究计划项目(2018JM6061);陕西省自然科学基础研究计划项目(2019JQ-166);陕西省重点研发计划一般项目(2019SF-272);陕西省教育厅自然科学专项(18JK0795);陕西省教育厅自然科学专项(19JK0842)
- DOI：10.3788/OPE.20192712.2730
  中图分类号： TP391; TP317
- 收稿日期：2019-08-12，
  
  录用日期：2019-10-22，
  
  纸质出版日期：2019-12-25
- 稿件说明：
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杨稳, 周明全, 耿国华, 等. 层次优化的颅骨点云配准[J]. 光学精密工程, 2019,27(12):2730-2739.

Wen YANG, Ming-quan ZHOU, Guo-hua GENG, et al. Hierarchical optimization of skull point cloud registration[J]. Optics and precision engineering, 2019, 27(12): 2730-2739.
杨稳, 周明全, 耿国华, 等. 层次优化的颅骨点云配准[J]. 光学精密工程, 2019,27(12):2730-2739. DOI： 10.3788/OPE.20192712.2730.

Wen YANG, Ming-quan ZHOU, Guo-hua GENG, et al. Hierarchical optimization of skull point cloud registration[J]. Optics and precision engineering, 2019, 27(12): 2730-2739. DOI： 10.3788/OPE.20192712.2730.

摘要

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。

Abstract

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.

关键词

Keywords

references

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