# Synthesizing a 4D Spatio-Angular Consistent Light Field from a Single Image

arXiv: https://arxiv.org/abs/1903.12364 · HTML: https://mlsanity.com/paper/1903.12364/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2019.

## Built on by (most cited first, 3 of 3)

- [Diffusion-based Light Field Synthesis](https://mlsanity.com/paper/2402.00575/) (2024)
- [Stereo-Knowledge Distillation from dpMV to Dual Pixels for Light Field Video Reconstruction](https://mlsanity.com/paper/2405.11823/) (2024)
- [Real-Time Position-Aware View Synthesis from Single-View Input](https://mlsanity.com/paper/2412.14005/) (2024)

## Abstract (arXiv)

Synthesizing a densely sampled light field from a single image is highly beneficial for many applications. The conventional method reconstructs a depth map and relies on physical-based rendering and a secondary network to improve the synthesized novel views. Simple pixel-based loss also limits the network by making it rely on pixel intensity cue rather than geometric reasoning. In this study, we show that a different geometric representation, namely, appearance flow, can be used to synthesize a light field from a single image robustly and directly. A single end-to-end deep neural network that does not require a physical-based approach nor a post-processing subnetwork is proposed. Two novel loss functions based on known light field domain knowledge are presented to enable the network to preserve the spatio-angular consistency between sub-aperture images effectively. Experimental results show that the proposed model successfully synthesizes dense light fields and qualitatively and quantitatively outperforms the previous model . The method can be generalized to arbitrary scenes, rather than focusing on a particular class of object. The synthesized light field can be used for various applications, such as depth estimation and refocusing.