Superquadric is an implicit model which was recently introduced and successfully applied in computer vision research. The authors introduce its generalization, the use of the hyperquadric models, for computer vision applications, and focus on its utilization for shape recovery from range data. The hyperquadric model can be composed of any number of terms. Its geometric bound is an arbitrary convex polyhedron, and thus it can describe more complex shapes than the superquadric. A fitting method is proposed that starts with a rough fit with only two terms in the 2-D case or three terms in the 3-D case, and then adds additional terms to improve the fit. The experiments indicate that the use of hyperquadrics is a promising paradigm for shape representation and recovery in computer vision.
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