Enhanced 3D representation using a hybrid model

This paper deals with generic 3D shape modelling for the purposes of object recognition. Difficulties with many existing methods are that they either capture insufficient detailed structure or fail to provide sufficiently abstract descriptions. The approach presented here attempts to address this problem by building a composite representation of the data in terms of a superquadric augmented with multi-scale surface models. This is illustrated experimentally using laser range data. The superquadric that results in the best possible fit is expressed in terms of its position, size, shape and pose parameters. The residual of the fit is then modelled at several scales using multiple surface patches with uniform mean and Gaussian curvature. A hierarchical ranking of these patches is used to describe the residual based on geometric properties. These geometric properties are ranked according to criteria expressing their stability and utility. The most stable patches are selected as the description of the residual. The resulting representation can then be used for both pose estimation and object recognition