Superquadric similarity measure with spherical harmonics in 3D object recognition

This paper proposes a novel approach for superquadric similarity measure in 3D object recognition. The 3D objects are represented by a composite volumetric representation of Superquadric (SQ)-based geons, which are the new and powerful volumetric models adequate for 3D recognition. The proposed approach is processed through three stages: first, a novel sampling algorithm is designed for searching Chebyshev nodes on superquadric surface to construct the discrete spherical function representing superquadric 3D shape; secondly, the fast Spherical Harmonic Transform is performed on the discrete spherical function to obtain the rotation invariant descriptor of superquadric; thirdly, the similarity of superquadrics is measured by computing the L-2 difference between two obtained descriptors. In addition, an integrated processing framework is presented for 3D object recognition with SQ-based geons from the real 3D data, which implements the approach proposed in this paper for shape similarity measure between SQ-based geons. Evaluation experiments demonstrate that the proposed approach is very efficient and robust for similarity measure of superquadric models. The research lays a foundation for developing SQ-based 3D object recognition systems.