Abstract
In this report, we present a 3D shape modeling method using the shape’s silhouettes from multiple views to determine the model (polyhedron) parameters. The polyhedron parameters are determined by neural networks, each of which represents the model’s silhouette observed from a view point, and determines the polyhedron parameters by the back propagation algorithm so that the model’s silhouette from each view approximates the corresponding silhouette of the target shape. By conducting basic experiments, we verified the effectiveness of the method.
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Kumazawa, I., Ohno, M. (2001). 3D Shape Reconstruction from Multiple Silhouettes: Generalization from Few Views by Neural Network Learning. In: Arcelli, C., Cordella, L.P., di Baja, G.S. (eds) Visual Form 2001. IWVF 2001. Lecture Notes in Computer Science, vol 2059. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45129-3_63
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DOI: https://doi.org/10.1007/3-540-45129-3_63
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