Probabilistic shape descriptor for triangulated surfaces

The importance of shape recognition is increasing rapidly in the field of computer graphics and multimedia communication because it is difficult to process information efficiently without its recognition. In this paper, we present a 3D object recognition approach based on a global geodesic measure. The key idea behind our methodology is to represent an object by a probabilistic shape descriptor that measures the global geodesic distance between two arbitrary points on the surface of an object. The geodesic distance has the advantage to be able to capture the intrinsic geometric structure of the data. Object matching can then be carried out by an information-theoretic dissimilarity measure calculations between geodesic shape distributions.

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