# Light Field Implicit Representation for Flexible Resolution Reconstruction

arXiv: https://arxiv.org/abs/2112.00185 · HTML: https://mlsanity.com/paper/2112.00185/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 52 references that are in the map.
- First posted on arXiv 30 Nov 2021.

## Builds on (most cited first, 30 of 32)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [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)
- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://mlsanity.com/paper/1812.03828/) (2018)
- [pixelNeRF: Neural Radiance Fields from One or Few Images](https://mlsanity.com/paper/2012.02190/) (2020)
- [HyperNetworks](https://mlsanity.com/paper/1609.09106/) (2016)
- [Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations](https://mlsanity.com/paper/1906.01618/) (2019)
- [PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization](https://mlsanity.com/paper/1905.05172/) (2019)
- [Convolutional Occupancy Networks](https://mlsanity.com/paper/2003.04618/) (2020)
- [Implicit Geometric Regularization for Learning Shapes](https://mlsanity.com/paper/2002.10099/) (2020)
- [Learning Continuous Image Representation with Local Implicit Image Function](https://mlsanity.com/paper/2012.09161/) (2020)
- [Conditional Neural Processes](https://mlsanity.com/paper/1807.01613/) (2018)
- [Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion](https://mlsanity.com/paper/2003.01456/) (2020)
- [DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction](https://mlsanity.com/paper/1905.10711/) (2019)
- [State of the Art on Neural Rendering](https://mlsanity.com/paper/2004.03805/) (2020)
- [Learning Shape Templates with Structured Implicit Functions](https://mlsanity.com/paper/1904.06447/) (2019)
- [Learned Initializations for Optimizing Coordinate-Based Neural Representations](https://mlsanity.com/paper/2012.02189/) (2020)
- [MetaSDF: Meta-learning Signed Distance Functions](https://mlsanity.com/paper/2006.09662/) (2020)
- [Learning-Based View Synthesis for Light Field Cameras](https://mlsanity.com/paper/1609.02974/) (2016)
- [Light Field Spatial Super-resolution via Deep Combinatorial Geometry Embedding and Structural Consistency Regularization](https://mlsanity.com/paper/2004.02215/) (2020)
- [X-Fields: Implicit Neural View-, Light- and Time-Image Interpolation](https://mlsanity.com/paper/2010.00450/) (2020)
- [Light Field Image Super-Resolution Using Deformable Convolution](https://mlsanity.com/paper/2007.03535/) (2020)
- [High-dimensional Dense Residual Convolutional Neural Network for Light Field Reconstruction](https://mlsanity.com/paper/1910.01426/) (2019)
- [Light Field Reconstruction Using Shearlet Transform](https://mlsanity.com/paper/1509.08969/) (2015)
- [Light Field Reconstruction Using Convolutional Network on EPI and Extended Applications](https://mlsanity.com/paper/2103.13043/) (2021)
- [Learning Light Field Angular Super-Resolution via a Geometry-Aware Network](https://mlsanity.com/paper/2002.11263/) (2020)
- [Spatial-Angular Attention Network for Light Field Reconstruction](https://mlsanity.com/paper/2007.02252/) (2020)
- [On-Demand Learning for Deep Image Restoration](https://mlsanity.com/paper/1612.01380/) (2016)
- [A Generative Model for Generic Light Field Reconstruction](https://mlsanity.com/paper/2005.06508/) (2020)

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

- [Progressive Multi-scale Light Field Networks](https://mlsanity.com/paper/2208.06710/) (2022)
- [MiNL: Micro-images based Neural Representation for Light Fields](https://mlsanity.com/paper/2209.08277/) (2022)
- [Continuous Levels of Detail for Light Field Networks](https://mlsanity.com/paper/2309.11591/) (2023)

## Abstract (arXiv)

Inspired by the recent advances in implicitly representing signals with trained neural networks, we aim to learn a continuous representation for narrow-baseline 4D light fields. We propose an implicit representation model for 4D light fields which is conditioned on a sparse set of input views. Our model is trained to output the light field values for a continuous range of query spatio-angular coordinates. Given a sparse set of input views, our scheme can super-resolve the input in both spatial and angular domains by flexible factors. consists of a feature extractor and a decoder which are trained on a dataset of light field patches. The feature extractor captures per-pixel features from the input views. These features can be resized to a desired spatial resolution and fed to the decoder along with the query coordinates. This formulation enables us to reconstruct light field views at any desired spatial and angular resolution. Additionally, our network can handle scenarios in which input views are either of low-resolution or with missing pixels. Experiments show that our method achieves state-of-the-art performance for the task of view synthesis while being computationally fast.