  Differentiable rendering is a very successful technique that applies to a
Single-View 3D Reconstruction. Current renderers use losses based on pixels
between a rendered image of some 3D reconstructed object and ground-truth
images from given matched viewpoints to optimise parameters of the 3D shape.
  These models require a rendering step, along with visibility handling and
evaluation of the shading model. The main goal of this paper is to demonstrate
that we can avoid these steps and still get reconstruction results as other
state-of-the-art models that are equal or even better than existing
category-specific reconstruction methods. First, we use the same CNN
architecture for the prediction of a point cloud shape and pose prediction like
the one used by Insafutdinov \& Dosovitskiy. Secondly, we propose the novel
effective loss function that evaluates how well the projections of
reconstructed 3D point clouds cover the ground truth object's silhouette. Then
we use Poisson Surface Reconstruction to transform the reconstructed point
cloud into a 3D mesh. Finally, we perform a GAN-based texture mapping on a
particular 3D mesh and produce a textured 3D mesh from a single 2D image. We
evaluate our method on different datasets (including ShapeNet, CUB-200-2011,
and Pascal3D+) and achieve state-of-the-art results, outperforming all the
other supervised and unsupervised methods and 3D representations, all in terms
of performance, accuracy, and training time.
