  The light field faithfully records the spatial and angular configurations of
the scene, which facilitates a wide range of imaging possibilities. In this
work, we propose an LF synthesis algorithm which renders high quality novel LF
views far outside the range of angular baselines of the given references. A
stratified synthesis strategy is adopted which parses the scene content based
on stratified disparity layers and across a varying range of spatial
granularities. Such a stratified methodology proves to help preserve scene
structures over large perspective shifts, and it provides informative clues for
inferring the textures of occluded regions. A generative-adversarial network
model is further adopted for parallax correction and occlusion completion
conditioned on the stratified synthesis features. Experiments show that our
proposed model can provide more reliable novel view synthesis quality at large
baseline extension ratios. Over 3dB quality improvement has been achieved
against state-of-the-art LF view synthesis algorithms.
