摘要
针对不同姿态下的三维可变形物体特征点一致性问题,提出了一种基于样例学习的特征点层次提取方法。该方法首先提取出三维模型的外部特征点;其次,根据外部特征点在不同姿态下所具有的局部特征相似性,采用热核信号和支持向量机识别出外部特征点的语义标签;最后,根据语义标签和测地距离,层次地提取出三维模型的其它语义特征点。实验结果表明,该方法能很好地得到三维可变形物体的各种语义上的有效特征点。
This paper addressed the problem about the consistency of feature points,with different posed of 3D deformable shapes.It proposed a new algorithm to hierarchically detect feature points,based on the learning samples.Firstly,the algorithm detects the external feature points from input 3D shapes.Secondly,according to the local similarity of external points under different postures,semantic tags of external point are recognized by heat kernel signature and support vector machine.Finally,other feature points are hierarchically extracted by combining semantic tags and the geodesic distance of external points.In experiment,the proposed algorithm is proven to be very robust in detecting semantic-aware feature points on deformable shapes.
出处
《计算机科学》
CSCD
北大核心
2014年第4期292-296,共5页
Computer Science
基金
国家自然科学基金(61272304)
浙江省自然科学基金(Y1110780)资助
关键词
三维模型特征点
层次提取
语义标签
支持向量机
热核信号
3D model feature point
Hierarchical extraction
Semantic tags
Support vector machine
Heat kernel signature
