Extracting Superquadric-based Geon Description for 3D Object Recognition

摘要

Geons recognition is one key issue in developing 3D object recognition system based on Recognition by components (RBC) theory. In this paper, we present a novel approach for extracting superquadric-based geon description of 3D volumetric primitives from real shape data, which integrates the advantages of deformable superquadric models reconstruction and SVM-based classification. First, Real-coded genetic algorithm (RCGA) is used for superquadric fitting to 3D data and the quantitative parametric information is obtained; then a new sophisticated feature set is derived from superquadric parameters obtained for the next step; and SVM-based classification is proposed and implemented for geons recognition and the qualitative geometric information is obtained. Furthermore, the knowledge-based feedback of SVM network is introduced for improving the classification performance. Ex-perimental results obtained show that our approach is efficient and precise for extracting superquadric-based geon description from real shape data in 3D object recognition. The results are very encouraging and have significant benefit for developing the general 3D object recognition system.