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3D model generation from single 2D RGB images is a challenging and activelyresearched computer vision task. Various techniques using conventional networkarchitectures have been proposed for the same. However, the body of researchwork is limited and there are various issues like using inefficient 3Drepresentation formats, weak 3D model generation backbones, inability togenerate dense point clouds, dependence of post-processing for generation ofdense point clouds, and dependence on silhouettes in RGB images. In this paper,a novel 2D RGB image to point cloud conversion technique is proposed, whichimproves the state of art in the field due to its efficient, robust and simplemodel by using the concept of parallelization in network architecture. It notonly uses the efficient and rich 3D representation of point clouds, but alsouses a novel and robust point cloud generation backbone in order to address theprevalent issues. This involves using a single-encoder multiple-decoder deepnetwork 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 iscompared with those of other state of the art techniques and impressive gainsin performance are demonstrated. Code is available athttps://github.com/mueedhafiz1982/

SE-MD: A Single-encoder multiple-decoder deep network for point cloud generation from 2D images