# Learning Occlusion-Aware View Synthesis for Light Fields

## TL;DR

This paper introduces a learning-based method for synthesizing novel views in light fields by estimating view-specific disparity maps, effectively handling occlusions and improving reconstruction quality near object boundaries.

## Contribution

The novel approach estimates a separate disparity map for each view, enhancing occlusion handling and view synthesis accuracy compared to existing methods.

## Key findings

- Outperforms state-of-the-art on Lytro light fields
- Effective in wide-baseline light field scenarios
- Handles occlusions near object boundaries successfully

## Abstract

In this work, we present a novel learning-based approach to synthesize new views of a light field image. In particular, given the four corner views of a light field, the presented method estimates any in-between view. We use three sequential convolutional neural networks for feature extraction, scene geometry estimation and view selection. Compared to state-of-the-art approaches, in order to handle occlusions we propose to estimate a different disparity map per view. Jointly with the view selection network, this strategy shows to be the most important to have proper reconstructions near object boundaries. Ablation studies and comparison against the state of the art on Lytro light fields show the superior performance of the proposed method. Furthermore, the method is adapted and tested on light fields with wide baselines acquired with a camera array and, in spite of having to deal with large occluded areas, the proposed approach yields very promising results.