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Conventional methods of 3D object generative modeling learn volumetricpredictions using deep networks with 3D convolutional operations, which aredirect analogies to classical 2D ones. However, these methods arecomputationally wasteful in attempt to predict 3D shapes, where information isrich only on the surfaces. In this paper, we propose a novel 3D generativemodeling framework to efficiently generate object shapes in the form of densepoint clouds. We use 2D convolutional operations to predict the 3D structurefrom multiple viewpoints and jointly apply geometric reasoning with 2Dprojection optimization. We introduce the pseudo-renderer, a differentiablemodule to approximate the true rendering operation, to synthesize novel depthmaps for optimization. Experimental results for single-image 3D objectreconstruction tasks show that we outperforms state-of-the-art methods in termsof shape similarity and prediction density.

Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction