摘要
特征线对三维模型的表达和识别具有重要意义,提出了符号曲面变化度的概念,其具备同时表达曲面弯曲程度和凹凸类型的能力,可以作为曲面曲率的良好近似.在此基础上,提出了一种基于符号曲面变化度与特征分区的特征线提取算法.首先选取点云中符号曲面变化度绝对值大于一定阈值的点作为潜在特征点;然后将符号曲面变化度作为区域增长限定条件对潜在特征点进行分割,并在通过局部曲面重建确定区域边界点后,采用基于曲面变化度和距离加权的双边滤波算法迭代细化边界点,以确定特征点真实位置;最后通过建立特征点的最小生成树实现特征线连接.实验结果表明,该算法能很好地识别、分割点云中的特征点,提取到准确、完整的特征线.
Feature line detection is important for the representing and understanding of 3D models. In this paper the concept of signed surface variation (SSV) is proposed. Except for the ability to represent local surface variation, SSV can also distinguish concavo surfaces from convex ones, so it is a good approxima-tion to surface curvature. Based on SSV and feature region segmentation, a novel point cloud feature line detection algorithm is present. Firstly, points with large absolute SSV are recognized as potential feature points; Then they are segmented to different regions with the guidance of SSV; On the next, local mesh sur-face of each region is reconstructed from which boundary points are recognized and iteratively thinned using bilateral filtering algorithm; Finally, feature lines are linked by constructing the minimal spanning tree of thinned boundary points. Experiments indicate that our algorithm can recognize and segment potential fea-ture points correctly, and can extract accurate feature lines completely.
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
2015年第12期2332-2339,共8页
Journal of Computer-Aided Design & Computer Graphics
基金
江苏省青年科学基金(BK20140892)
南京邮电大学校引进人才科研启动基金(NY213038)
关键词
点云
特征线
曲面变化度
曲率
point cloud
feature line
surface variation
curvature
