牙颌点云数据的显著性特征提取

Significant Feature Extraction for the Dental Point Cloud Data

随着激光扫描测量技术的发展,其数据测量精度的逐渐增高使得获取的几何模型表面点云数据的细节信息越丰富,能更准确的反应物体几何表面特征,但如此海量的点云数据同时也带来对应的技术挑战,海量的点云数据在计算机文件存储、数据后期进一步处理以及软件可视化方面都不方便且效率低下.本文中的算法首先采用栅格法对点云进行空间划分及领域关系的建立,其次利用局部表面拟合的方法估算点云法向量,然后利用点云K领域法的向量求解坐标点的显著性值,最后根据显著性的值构建点云八叉树.该算法实现了对点云显著性特征的提取和对点云数据量的进一步简化,它不仅保留了对点云细节特征保持方面的优势,而且在时间效率上得到了提高.

With the development of laser scanning measurement technology, the detailed information about the surface point cloud data of the geometric model is more abundant due to the more efficient data detection accuracy, make it more precise to show the surface features of objects. However, the corresponding technical challenges may appear at the same time because of such a large amount of point cloud data, which can be used in the computer file storage, data post-processing and software visualization inconveniently and inefficiently. A new algorithm is introduced in this paper. Firstly, we make a space division for point cloud data and establish the domain relationship using the grid method. Secondly, we estimate the point cloud normal vector by means of local surface fitting. Thirdly, we find out the significant value of the coordinate points using the point cloud K field method. Finally, we achieve the point cloud octree according to the significant value. In a word, this algorithm realizes the goal that the significant features of the point cloud can be extracted and the amount of the point cloud data can be simplified. Not only does it retain the advantages of the detail characteristics of the point cloud, but also make it more effective.