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<p><b>Abstract</b>—We present a novel approach to reliable and efficient recovery of part-descriptions in terms of superquadric models from range data. We show that superquadrics can <it>directly</it> be recovered from unsegmented data, thus avoiding any presegmentation steps (e.g., in terms of surfaces). The approach is based on the <it>recover-and-select</it> paradigm [<ref rid="bibi128910" type="bib">10</ref>]. We present several experiments on real and synthetic range images, where we demonstrate the stability of the results with respect to viewpoint and noise.</p>
Range image segmentation, recover-and-select paradigm, recovery of volumetric models, superquadrics.

A. Leonardis, A. Jaklic and F. Solina, "Superquadrics for Segmenting and Modeling Range Data," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 19, no. , pp. 1289-1295, 1997.
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