3D object classification system based on volumetric parts

This paper presents a 3D object classification system based on volumetric parts. As the constituents of 3D object, the parts are described by superquadric-based geons, which enables a more compact 3D object representation. In the developed classification system, the improved interpretation tree method is implemented for classification, where a set of novel integrated features and corresponding constraints are proposed, which not only reflect individual parts´ shape, but model´s topological structure among 3D parts. The constraints are used to define efficient interpretation tree search rules, and the feasible correspondences of unknown object data and the stored models are obtained. Then a similarity measure computation algorithm is proposed to evaluate the shape similarity of the correspondence. The classification system can achieve both whole match and partial match between unknown object data and 3D models with shape similarity ranks; particularly, focus match can be accomplished, in which different key parts may be labeled and all the matched models with corresponding key parts can be obtained. The performance of the presented 3D object classification system is evaluated with a series of experiments.