# Iterative approach to reconstructing neural disparity fields from light-field data

arXiv: https://arxiv.org/abs/2407.15380 · HTML: https://mlsanity.com/paper/2407.15380/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 69 references that are in the map.
- First posted on arXiv 22 Jul 2024.

## Builds on (most cited first, 25 of 25)

- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020)
- [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://mlsanity.com/paper/1812.03828/) (2018)
- [Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021)
- [Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields](https://mlsanity.com/paper/2103.13415/) (2021)
- [Plenoxels: Radiance Fields without Neural Networks](https://mlsanity.com/paper/2112.05131/) (2021)
- [TensoRF: Tensorial Radiance Fields](https://mlsanity.com/paper/2203.09517/) (2022)
- [Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fields](https://mlsanity.com/paper/2112.03907/) (2021)
- [Neural Fields in Visual Computing and Beyond](https://mlsanity.com/paper/2111.11426/) (2021)
- [Factor Fields: A Unified Framework for Neural Fields and Beyond](https://mlsanity.com/paper/2302.01226/) (2023)
- [Learning Neural Light Fields with Ray-Space Embedding Networks](https://mlsanity.com/paper/2112.01523/) (2021)
- [EPINET: A Fully-Convolutional Neural Network Using Epipolar Geometry for Depth from Light Field Images](https://mlsanity.com/paper/1804.02379/) (2018)
- [Disentangling Light Fields for Super-Resolution and Disparity Estimation](https://mlsanity.com/paper/2202.10603/) (2022)
- [NeuLF: Efficient Novel View Synthesis with Neural 4D Light Field](https://mlsanity.com/paper/2105.07112/) (2021)
- [DINER: Disorder-Invariant Implicit Neural Representation](https://mlsanity.com/paper/2211.07871/) (2022)
- [Occlusion-Aware Cost Constructor for Light Field Depth Estimation](https://mlsanity.com/paper/2203.01576/) (2022)
- [Accurate Light Field Depth Estimation with Superpixel Regularization over Partially Occluded Regions](https://mlsanity.com/paper/1708.01964/) (2017)
- [The Implicit Values of A Good Hand Shake: Handheld Multi-Frame Neural Depth Refinement](https://mlsanity.com/paper/2111.13738/) (2021)
- [Learning Sub-Pixel Disparity Distribution for Light Field Depth Estimation](https://mlsanity.com/paper/2208.09688/) (2022)
- [EPI-based Oriented Relation Networks for Light Field Depth Estimation](https://mlsanity.com/paper/2007.04538/) (2020)
- [Shakes on a Plane: Unsupervised Depth Estimation from Unstabilized Photography](https://mlsanity.com/paper/2212.12324/) (2022)
- [Neural Radiance Fields: Past, Present, and Future](https://mlsanity.com/paper/2304.10050/) (2023)
- [OPAL: Occlusion Pattern Aware Loss for Unsupervised Light Field Disparity Estimation](https://mlsanity.com/paper/2203.02231/) (2022)
- [Matching entropy based disparity estimation from light field](https://mlsanity.com/paper/2210.15948/) (2022)

## Built on by (most cited first, 2 of 2)

- [Frequency-Structured Field Learning for Light-Field Disparity Estimation](https://mlsanity.com/paper/2607.14941/) (2026)
- [Fast Implicit Neural Light Field Representation via Geometric Decomposition and Multi-Resolution Low-Rank Features](https://mlsanity.com/paper/2608.13949/) (2026)

## Abstract (arXiv)

This study proposes a neural disparity field (NDF) that establishes an implicit, continuous representation of scene disparity based on a neural field and an iterative approach to address the inverse problem of NDF reconstruction from light-field data. NDF enables seamless and precise characterization of disparity variations in three-dimensional scenes and can discretize disparity at any arbitrary resolution, overcoming the limitations of traditional disparity maps that are prone to sampling errors and interpolation inaccuracies. The proposed NDF network architecture utilizes hash encoding combined with multilayer perceptrons to capture detailed disparities in texture levels, thereby enhancing its ability to represent the geometric information of complex scenes. By leveraging the spatial-angular consistency inherent in light-field data, a differentiable forward model to generate a central view image from the light-field data is developed. Based on the forward model, an optimization scheme for the inverse problem of NDF reconstruction using differentiable propagation operators is established. Furthermore, an iterative solution method is adopted to reconstruct the NDF in the optimization scheme, which does not require training datasets and applies to light-field data captured by various acquisition methods. Experimental results demonstrate that high-quality NDF can be reconstructed from light-field data using the proposed method. High-resolution disparity can be effectively recovered by NDF, demonstrating its capability for the implicit, continuous representation of scene disparities.