网格曲面特征的稀疏性优化检测算法  

Sparsity Optimized Mesh Feature Detection

作　　者：王伟明[1] 刘秀平[1] 杨周旺[2] 刘利刚[2] 

机构地区：[1]大连理工大学数学科学学院,大连116024 [2]中国科学技术大学数学科学学院,合肥230026

出　　处：《计算机辅助设计与图形学学报》2013年第8期1128-1136,共9页Journal of Computer-Aided Design & Computer Graphics

基　　金：国家自然科学基金重点项目(U0935004);国家自然科学基金(61173102)

摘　　要：针对现有的特征检测算法大多数基于微分几何量,对噪声比较敏感、运行速度比较慢、对于过渡特征处理得不够理想的问题,提出一种基于稀疏性优化的网格特征检测算法.该算法主要包括3个过程:首先利用带l1范数稀疏性约束项和l2范数误差项的Laplacian能量函数对网格进行光顺,得到光顺后网格顶点的移动距离;然后根据顶点的移动距离提取初始特征点;最后对提取的特征点进行后处理,使得特征点更为完整.其中,l1范数稀疏性约束项用来约束发生移动点的数目;l2范数的误差约束项用来控制光顺后模型的退化程度.该算法易于实现,能够处理尖锐特征、弱特征和过渡特征.与基于微分几何量的特征提取方法相比,文中算法不仅简单有效、运行时间短,而且提取的特征线也更好.Most of the existing feature detection methods are differential geometry based, sensitive to noise, run slowly and cannot handle blend features very well. To solve these problems, a sparsity optimization based mesh feature detection method is proposed in this paper. This approach mainly consists of three procedures： first, the mesh is smoothed by a Laplacian energy function restrained by a l1-norm sparsity term and a/z-norm error term; second, initial feature points are extracted according to the moving distances; last, a post-processing is performed on the extracted feature points so that these features look better. In our approach, the/1-norm is used to penalize the number of points moved and the/2-norm is applied to control the degradation of the smoothed model. This method is easy to implement, and it can not only handle sharp features and weak features, but also deal with blend features. Compared with differential geometry based methods, the results show that the proposed method is simple, effective, and fast. Moreover, the extracted feature lines are superior to other methods.

关 键 词：网格光顺 过渡特征 伪特征 稀疏性 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]