  A key question in the problem of 3D reconstruction is how to train a machine
or a robot to model 3D objects. Many tasks like navigation in real-time systems
such as autonomous vehicles directly depend on this problem. These systems
usually have limited computational power. Despite considerable progress in 3D
reconstruction systems in recent years, applying them to real-time systems such
as navigation systems in autonomous vehicles is still challenging due to the
high complexity and computational demand of the existing methods. This study
addresses current problems in reconstructing objects displayed in a single-view
image in a faster (real-time) fashion. To this end, a simple yet powerful deep
neural framework is developed. The proposed framework consists of two
components: the feature extractor module and the 3D generator module. We use
point cloud representation for the output of our reconstruction module. The
ShapeNet dataset is utilized to compare the method with the existing results in
terms of computation time and accuracy. Simulations demonstrate the superior
performance of the proposed method.
  Index Terms-Real-time 3D reconstruction, single-view reconstruction,
supervised learning, deep neural network
