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In this paper, we present a neural-network learning schemeforfacereconstruction.Thisscheme,whichwe called as Smooth Projected Polygon Representation Neural Network (SPPRNN), is able to successively refinethepolygon'sverticesparameterofaninitial3D shapebasedondepth-mapsofseveralcalibratedimages taken from multiple views. The depth-maps, which are obtained by(More)
This paper presents a new neural network (NN) scheme for recovering three dimensional (3D) transparent surface. We view the transparent surface modeling, not as a separate problem, but as an extension of opaque surface modeling. The main insight of this work is we simulate transparency not only for generating visually realistic images, but for recovering(More)
This paper presents a combinatorial (decision tree induction) technique for transparent surface modeling from polarization images. This technique simultaneously uses the object's symmetry, brewster angle, and degree of polarization to select accurate reference points. The reference points contain information about surface's normals position and direction at(More)
This paper proposes a second-order discrete total generalized variation (TGV) for arbitrary graph signals, which we call the graph TGV (G-TGV). The original TGV was introduced as a natural higher-order extension of the well-known total variation (TV) and is an effective prior for piecewise smooth signals. Similarly, the proposed G-TGV is an extension of the(More)