Robust Light Field Synthesis From Stereo Images With Left-Right Geometric Consistency

We propose a lightweight yet effective deep learning pipeline for light field synthesis from a single stereo image pair. Our pipeline consists of a convolutional network (CNN) that enforces a left-right consistency constraint on the light fields synthesized from left and right stereo views, a stage that merges light fields synthesized from left and right stereo views with a novel alpha blending technique, and a final refinement network using a unique 3D convolution operation. Our experiments quantitatively and qualitatively confirm the effectiveness and robustness of the proposed model, which performs favorably against state-of-the-art algorithms for light field synthesis from extremely sparse (only one, two, or four) views while using much fewer parameters.