Efficient Variational Light Field View Synthesis for Making Stereoscopic 3D Images

We present a novel approach for making stereoscopic images by variational view synthesis on the multi-perspective light field. With the intended disparities as constraints, we specialize the generative variational model by incorporating per-pixel viewpoint assignment to synthesize the stereo pair. Also, we improve the variational solution by use of explicit weighted average on the light field. Our algorithm is able to handle arbitrary disparity remapping, thus enabling more flexible disparity control for the desired stereoscopic effect. The experiments demonstrate the effectiveness and efficiency for making the stereoscopic 3D images based on the light field.