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The authors introduce a surface representation for recognizing curved objects. The approach begins by representing an object by a discrete mesh of points built from range data or from a geometric model of the object. The mesh is computed from the data by deforming a standard shaped mesh, for example, an ellipsoid, until it fits the surface of the object. Local regularity constraints that the mesh must satisfy are defined. A canonical mapping is then defined between the mesh describing the object and a standard spherical mesh. A surface curvature index which is pose-invariant is stored at every node of the mesh. This object representation is used for recognition by comparing the spherical model of a reference object with the model extracted from a new observed scene. It is shown that the similarity between reference model and observed data can be evaluated, and it is also demonstrated that the pose of the reference object in the observed scene can be easily computed using this representation.>
Delingette et al. (Mon,) studied this question.