3-D shape approximation using parametric geons

  • Authors:

  • Kenong Wu;Martin D. Levine

  • Affiliations:

  • Centre for Intelligent Machines, McGill University, 3480 University Street, Montreal, Quebec, Canada H3A 2A7;Centre for Intelligent Machines, McGill University, 3480 University Street, Montreal, Quebec, Canada H3A 2A7

  • Venue:
  • Image and Vision Computing
  • Year:
  • 1997

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Abstract

This paper presents a new approach to 3D shape representation - approximating the shapes of object parts by a set of prescribed volumetric models using single- and multi-view range data. We define a new set of volumetric part models, called parametric geons. These are seven qualitative shapes, each of which is formulated by a restricted globally-deformed superellipsoid. Model recovery is performed by fitting all parametric geons to a part and selecting the best model for the part based on the minimum fitting residual. A newly-defined objective function and a fast global optimisation technique are employed to obtain robust model fitting results. Parametric geons provide a global shape constraint that allows model recovery to explicitly verify the resultant part descriptions. Through systematic experiments, we examine the efficiency of the objective function, the discriminative ability of parametric geons, the effects of object shape imperfection to model recovery, and the importance of multiview data for shape approximation. The experimental results demonstrate that this approach can successfully recover qualitative shape models from object parts, especially when a part shape is not fully consistent with model shapes.