一种新的基于特殊离群样本优化的三维点云特征选择算法

A new 3D point clouds feature selection method using specific outliers optimization

随着元宇宙、数字孪生、虚拟现实与增强现实等前沿技术的快速发展,三维点云在电力、建筑、先进制造等行业中得到广泛应用,随之而来的,如何降低三维点云数据冗余度、有效进行点云特征选择,已在充分利用海量点云数据中扮演着关键角色。考虑到现有大多数三维点云特征选择算法忽略了特定样本在特征评估中的表现,提出一种新的有监督特征选择算法,即基于特殊离群样本优化的特征选择算法(FSSO)。具体地,为获得精准的特殊离群样本(SOs),FSSO优化均值中心并动态地界定类簇主体;计算SOs的类内相对偏离程度,通过减小类内相对偏离对特征进行打分,实现特征选择过程。在3个公共的三维点云模型分类数据集上(ModelNet40,IntrA,ShapeNetCore)的实验,以及4个高维人工特征数据集的验证实验结果表明,相较于其他特征选择算法,FSSO可选择出具有更强分类能力的特征子集,并提升分类准确率。

With the rapid development of technologies in metaverse,digital twins,virtual and augmented reality,three-dimensional(3D)point clouds have been widely applied to electric power,construction,advanced manufacturing,and other industries.As a result,how to reduce the redundancies of 3D point clouds data and how to effectively select useful point cloud features have played a critical role in the full use of massive point clouds data.Considering that most of the current feature selection methods pay little attention to specific instances,in this paper,we proposed a novel supervised feature selection method,named feature selection based on specific outliers optimization(FSSO).Specifically,in order to obtain accurate specific outliers(SOs),we first optimized the traditional mean center of class,and automatically defined the class majority.Then,we proposed the feature selection algorithm that could compute the intra-class relative deviation of SOs,and score features based on the deviations.Extensive experiments on 3D data clouds classification datasets(ModelNet40,IntrA,and ShapeNetCore),and on four high-dimensional handcrafted datasets show that the proposed FSSO can select discriminative features,and improve the classification accuracy.