Automatic 2D to stereoscopic video conversion for 3D TVS

In this paper we present a novel technique for automatically converting 2D videos to stereoscopic. Uniquely, the proposed approach leverages the strengths of Deep Learning to address the complex problem of depth estimation from a single image. A Convolutional Neural Network is trained on input RGB images and their corresponding depths maps. We reformulate and simplify the process of generating the second camera's depth map and present how this can be used to render an anaglyph image. The anaglyph image was used for demonstration only because of the easy and wide availability of red/cyan glasses however, this does not limit the applicability of the proposed technique to other stereo forms. Finally, we present preliminary results and discuss the challenges.