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<p>An algorithm for the reconstruction of visual surfaces from sparse data is proposed. An important aspect of this algorithm is that the surface estimated from the sparse data is approximately invariant with respect to rigid transformation of the surface in 3D space. The algorithm is based on casting the problem as an ill-posed inverse problem that must be stabilized using a priori information related to the image and constraint formation. To form a surface estimate that is approximately invariant with respect to viewpoint, the stabilizing information is based on invariant surface characteristics. With appropriate approximations, this results in a convex functional to minimize, which is then solved using finite element analysis. The relationship of this algorithm to several previously proposed reconstruction algorithms is discussed, and several examples that demonstrate its effectiveness in reconstructing viewpoint-invariant surface estimates are given.</p>
viewpoint invariant recovery; picture processing; pattern recognition; visual surfaces; sparse data; 3D space; ill-posed inverse problem; invariant surface characteristics; finite element analysis; finite element analysis; pattern recognition; picture processing

R. Stevenson and E. Delp, "Viewpoint Invariant Recovery of Visual Surfaces from Sparse Data," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 14, no. , pp. 897-909, 1992.
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