# Learning Occlusion-Aware View Synthesis for Light Fields

arXiv: https://arxiv.org/abs/1905.11271 · HTML: https://mlsanity.com/paper/1905.11271/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2019.

## Built on by (most cited first, 1 of 1)

- [SE-MD: A Single-encoder multiple-decoder deep network for point cloud generation from 2D images](https://mlsanity.com/paper/2106.15325/) (2021)

## Abstract (arXiv)

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.