# Stratified Labeling for Surface Consistent Parallax Correction and Occlusion Completion

## TL;DR

This paper introduces a stratified synthesis approach combined with a GAN model to improve light field view synthesis, especially for large perspective shifts, achieving significant quality enhancements over existing methods.

## Contribution

The paper presents a novel stratified disparity layer-based synthesis strategy integrated with a GAN for enhanced parallax correction and occlusion completion in light field view synthesis.

## Key findings

- Achieves over 3dB quality improvement compared to state-of-the-art methods.
- Provides more reliable novel view synthesis at large baseline extensions.
- Effectively preserves scene structures over large perspective shifts.

## Abstract

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.