Conventional methods of 3D object generative modeling learn volumetric
predictions using deep networks with 3D convolutional operations, which are
direct analogies to classical 2D ones. However, these methods are
computationally wasteful in attempt to predict 3D shapes, where information is
rich only on the surfaces. In this paper, we propose a novel 3D generative
modeling framework to efficiently generate object shapes in the form of dense
point clouds. We use 2D convolutional operations to predict the 3D structure
from multiple viewpoints and jointly apply geometric reasoning with 2D
projection optimization. We introduce the pseudo-renderer, a differentiable
module to approximate the true rendering operation, to synthesize novel depth
maps for optimization. Experimental results for single-image 3D object
reconstruction tasks show that we outperforms state-of-the-art methods in terms
of shape similarity and prediction density
