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We propose to represent shapes as the deformation and combination of learnable elementary 3D structures, which are primitives resulting from training over a collection of shape.
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Learning Part-based Templates from Large Collections of 3D Shapes
V. G. Kim, W. Li, N. J. Mitra, S. Chaudhuri, S. DiVerdi, and T. Funkhouser · 2013
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T. Groueix, M. Fisher, V. G. Kim, B. Russell, and M. Aubry · 2018
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AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation
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Supervised fitting of geometric primitives to 3d point clouds
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Foldingnet: Point cloud auto-encoder via deep grid deformation
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Learning shape templates with structured implicit functions
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