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Rendering bridges the gap between 2D vision and 3D scenes by simulating the physical process of image formation.
We advocate the use of implicit fields for learning generative models of shapes and introduce an implicit field decoder, called IM-NET, for shape generation, aimed at improving the visual quality of the generated shapes.
Reconstructing 3D shapes from single-view images has been a long-standing research problem.
3D reconstruction from single view images is an ill-posed problem.