A Hierarchical Skull Point Cloud Registration Method
A Hierarchical Skull Point Cloud Registration Method
复制标题
一种分层头骨点云配准方法
DOI：
10.1109/access.2019.2940793
复制
发表时间：
2019
期刊：
影响因子：
3.9
通讯作者：
Liu Xiaoning
中科院分区：
文献类型：
--
作者：
Yang Wen;Zhou Mingquan;Geng Guohua;Liu Xiaoning
颅骨配准是颅面重建的重要步骤之一，其配准精度和效率对重建结果产生重要影响。针对现有头骨配准方法精度和效率较低的问题，提出一种分层头骨点云配准方法。整个登记过程分为粗登记阶段和精登记阶段。首先，从预处理的头骨点云模型中提取特征点，并根据特征点及其邻居点建立局部坐标参考系。改进的自旋图像用于构造局部特征描述符。根据最近邻算法进行特征匹配，并利用k-means算法消除不匹配点，实现头骨粗配准。然后，在粗配准的基础上，采用改进的ICP算法实现头骨的精细配准。在此过程中，我们使用随机采样来减少点的搜索规模，并添加几何特征约束以进一步消除不匹配的点。最后将整个配准算法应用到头骨点云数据上进行验证。实验结果表明，与其他方法相比，该方法的配准效果和效率均优于其他方法。为了验证该方法的普适性，我们还使用通用数据集进行验证。实验证明该方法也是非常有效的。
Skull registration is one of the important steps in craniofacial reconstruction, and its registration accuracy and efficiency have an important impact on the reconstruction results. To solve the problem of low accuracy and efficiency of existing skull registration methods, a hierarchical skull point cloud registration method is proposed in this paper. The whole registration process is divided into a rough registration stage and a fine registration stage. Firstly, feature points are extracted from the pre-processed skull point cloud model, and a local coordinate reference system is established according to the feature points and their neighbor points. The improved spin image is used to construct the local feature descriptor. The feature matching is carried out according to the nearest neighbor algorithm, and the k-means algorithm is used to eliminate the mismatching points to achieve skull rough registration. Then, based on rough registration, we use an improved ICP algorithm to achieve fine registration of the skull. In this process, we use random sampling to reduce the search scale of points and add geometric feature constraints to further eliminate mismatched points. Finally, the whole registration algorithm is applied to the skull point cloud data to verify. The experimental results show that, compared with other methods, the registration effect and efficiency of the proposed method are superior to those of other methods. In order to verify the universality of the method, we also use a common data set for verification. Experiments show that the method is also very effective.