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.
@cite_24: 
@cite_37: In this paper we present a set of efficient image based rendering methods capable of rendering multiple frames per second on a PC. The first method warps Sprites with Depth representing smooth surfaces without the gaps found in other techniques. A second method for more general scenes performs warping from an intermediate representation called a Layered Depth Image (LDI). An LDI is a view of the scene from a single input camera view, but with multiple pixels along each line of sight. The size of the representation grows only linearly with the observed depth complexity in the scene. Moreover, because the LDI data are represented in a single image coordinate system, McMillan’s warp ordering algorithm can be successfully adapted. As a result, pixels are drawn in the output image in back-to-front order. No z-buffer is required, so alphacompositing can be done efficiently without depth sorting. This makes splatting an efficient solution to the resampling problem.
@cite_0: We address the problem of synthesizing new views from stereo images. We propose a new representation – "multiplane image" – and learn such representations using large amounts of training video.
Scene content at different depth show complex occlusion relationships when viewed from distant viewing angles. It is a well-adopted strategy to segment the pixels into separate layers based on their motion @cite_24 and disparity range @cite_37 @cite_0 for independent processing. Notably, @cite_0 proposed an end-to-end training framework that first leans the Multi-Plane Images (MPI) with increasing depth ranges. The inferred MPIs are then used to synthesize a range of novel views via a subsequent module. Such a framework preserves the scene geometry in an computationally efficient way, however it produces noticeable distortions when the parallax shift is large; and it is beyond its capability to deal with large ambiguous regions caused by occlusion.