基于改进解释树的三维物体分类
3D Object Classification Based on Improved Interpretation Tree
-
摘要: 为了实现对未知三维物体的分类,提出了一种基于改进解释树的三维物体分类方法,将未知物体分到一组预先定义的物体类中.在该方法中,提出了一组新的、完善的三维物体形状特征及对应的约束,定义了有效的解释树约束搜索规则,能快速得到待分类物体和三维模型之间的匹配关系;设计了形状相似性度量计算算法,得到待分类物体与三维模型之间的形状相似度.该分类方法能实现多种类型的匹配计算,得到具有模型形状相似度排序的分类结果和未知物体所属的类别.大量的实验结果充分表明了该三维物体分类方法的良好性能.Abstract: In order to classify unknown 3D objects into a set of pre-determined object classes,the paper proposes a 3D object classification approach based on improved interpretation tree.In our approach,a set of novel integrated features and corresponding constraints are presented,which are used to define the efficient tree search rules,and obtain the feasible correspondences of object data and the stored models.A similarity measure computation algorithm is developed to evaluate the shape similarity of the correspondence.The classification approach can achieve different kinds of matches with the model similarity measures and the object class which the unknown object belongs to.The good performance of the proposed 3D object classification approach is demonstrated with a series of experiments.