摘要
目的点云配准是计算机视觉领域里的一个研究热点,其应用领域涉及3维重建、目标识别、颅面复原等多个方面。颅骨配准是颅面复原的一个重要步骤,其配准的正确与否将直接影响到颅面复原的结果。为了提高颅骨配准的精度和收敛速度,提出一种基于局部特征的颅骨点云模型配准方法。方法首先提取颅骨点云模型的局部深度、法线偏角和点云密度等局部特征;然后计算局部特征点集的相关性,得到相关候选点集,并通过删减外点实现颅骨点云的粗配准;最后采用基于高斯概率模型和动态迭代系数的改进迭代最近点(ICP)算法实现颅骨的细配准。结果通过对公共点云数据模型以及颅骨点云数据模型分别进行配准实验,结果表明,基于局部特征的点云配准算法可以完成点云模型的精确配准,特别是对颅骨点云模型具有较好的配准效果。在颅骨细配准阶段,跟ICP算法相比,改进ICP算法的配准精度和收敛速度分别提高了约30%和60%;跟概率迭代最近点(PCP)算法相比,其配准精度差异不大,收敛速度提高了约50%。结论基于局部特征的点云配准算法不仅可以用于公共点云数据模型的精确配准,而且更适用于颅骨点云数据模型的配准,是一种精度高、速度快的颅骨点云模型配准方法。
Abstract： Objective Point cloud registration with numerous applications, including 3D modeling, object recognition, scene understanding, 3D shape detection, and craniofacial reconstruction, is a significant and active research topic in computer vision. A 3D scanner can only obtain the partial 3D point cloud of one object associated with a single coordinate system from one viewpoint. The 3D point clouds captured from different viewpoints must be transformed into a common co- ordinate system according to rigid transformations to reconstruct the overall 3D shape. The 3D point cloud registration aims to compute the rigid transformation between 3D point clouds and automatically obtain the complete 3D shape of the object. Skull registration is an important step in craniofacial reconstruction. The correctness of its registration will directly affect the result of craniofacial reconstruction. Skull registration is the process of searching for one or more reference skulls from the existing skull database that is most similar to an unknown skull. The face of the reference skull can be used as the ref- erence face of the unknown skull to provide a possible basis for craniofacial reconstruction. Thus far, most of the skull reg- istration methods are feature-based method that contains two methods, namely, global and local feature-based methods. The extraction of feature descriptors is extremely important for registration. The global feature descriptor performs excellent discrimination for complete object representation, whereas the local feature descriptor is more robust against noise and out- liers. The local feature descriptor is more suitable for the skull model registration than the global feature descriptor because of the complexity of skull point cloud model. Among the local feature descriptors, 3D point-based descriptor has been widely applied to represent a partial object because of its excellent generalization. The 3D point-based descriptor encodes the information of neighboring points of an interest point in a com
出处
《中国图象图形学报》
CSCD
北大核心
2017年第8期1120-1127,共8页
Journal of Image and Graphics
基金
国家自然科学基金项目(61373117
61305032)
咸阳师范学院专项科研基金项目(XSYK17037)~~
关键词
颅面复原
颅骨配准
局部特征
迭代最近点
高斯概率模型
动态迭代系数
craniofacial reconstruction
skull registration
local feature
iterative closest point
Gaussian probability model
active iterative coefficient
