<p>One of the most important aspects of visual perception is inference of 3D shape from a 2D retinal image of the real world. The existence of several valid mapping functions from object to data makes this inverse problem ill-posed and therefore computationally difficult. In human vision, the retinal image is a 2D projection of the 3D real world. The visual system imposes certain constraints on the family of solutions in order to efficiently solve this inverse problem. This project specifically focuses on the aspect of minimization of standard deviation of all 3D angles (MSDA) for 3D perception. Our goal is to use a Deep Convolutional Neural Network based on biological principles derived from visual area V4 to solve 3D reconstruction using constrained minimization of MSDA. We conduct an experiment with novel shapes with human participants to collect data to test our model.</p>