  3D model generation from single 2D RGB images is a challenging and actively
researched computer vision task. Various techniques using conventional network
architectures have been proposed for the same. However, the body of research
work is limited and there are various issues like using inefficient 3D
representation formats, weak 3D model generation backbones, inability to
generate dense point clouds, dependence of post-processing for generation of
dense point clouds, and dependence on silhouettes in RGB images. In this paper,
a novel 2D RGB image to point cloud conversion technique is proposed, which
improves the state of art in the field due to its efficient, robust and simple
model by using the concept of parallelization in network architecture. It not
only uses the efficient and rich 3D representation of point clouds, but also
uses a novel and robust point cloud generation backbone in order to address the
prevalent issues. This involves using a single-encoder multiple-decoder deep
network architecture wherein each decoder generates certain fixed viewpoints.
This is followed by fusing all the viewpoints to generate a dense point cloud.
Various experiments are conducted on the technique and its performance is
compared with those of other state of the art techniques and impressive gains
in performance are demonstrated. Code is available at
https://github.com/mueedhafiz1982/
