# A Fourier Disparity Layer representation for Light Fields

arXiv: https://arxiv.org/abs/1901.06919 · HTML: https://mlsanity.com/paper/1901.06919/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jan 2019.

## Built on by (most cited first, 9 of 9)

- [A Hierarchical Coding Scheme for Glasses-free 3D Displays Based on Scalable Hybrid Layered Representation of Real-World Light Fields](https://mlsanity.com/paper/2104.09378/) (2021)
- [A Novel Hierarchical Light Field Coding Scheme Based on Hybrid Stacked Multiplicative Layers and Fourier Disparity Layers for Glasses-Free 3D Displays](https://mlsanity.com/paper/2108.12399/) (2021)
- [Deep Anti-aliasing of Whole Focal Stack Using Slice Spectrum](https://mlsanity.com/paper/2101.09420/) (2021)
- [A learning-based view extrapolation method for axial super-resolution](https://mlsanity.com/paper/2103.06510/) (2021)
- [Fristograms: Revealing and Exploiting Light Field Internals](https://mlsanity.com/paper/2107.10563/) (2021)
- [Spectral analysis of re-parameterized light fields](https://mlsanity.com/paper/2110.06064/) (2021)
- [Epipolar Focus Spectrum: A Novel Light Field Representation and Application in Dense-view Reconstruction](https://mlsanity.com/paper/2204.00193/) (2022)
- [Ray-Space Motion Compensation for Lenslet Plenoptic Video Coding](https://mlsanity.com/paper/2207.00522/) (2022)
- [STAR: Structure-aware Test-time Adaptation for diffusion-based light field Reconstruction](https://mlsanity.com/paper/2609.19747/) (2026)

## Abstract (arXiv)

In this paper, we present a new Light Field representation for efficient Light Field processing and rendering called Fourier Disparity Layers (FDL). The proposed FDL representation samples the Light Field in the depth (or equivalently the disparity) dimension by decomposing the scene as a discrete sum of layers. The layers can be constructed from various types of Light Field inputs including a set of sub-aperture images, a focal stack, or even a combination of both. From our derivations in the Fourier domain, the layers are simply obtained by a regularized least square regression performed independently at each spatial frequency, which is efficiently parallelized in a GPU implementation. Our model is also used to derive a gradient descent based calibration step that estimates the input view positions and an optimal set of disparity values required for the layer construction. Once the layers are known, they can be simply shifted and filtered to produce different viewpoints of the scene while controlling the focus and simulating a camera aperture of arbitrary shape and size. Our implementation in the Fourier domain allows real time Light Field rendering. Finally, direct applications such as view interpolation or extrapolation and denoising are presented and evaluated.