# Coded-E2LF: Coded Aperture Light Field Imaging from Events

arXiv: https://arxiv.org/abs/2602.22620 · HTML: https://mlsanity.com/paper/2602.22620/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 61 references that are in the map.
- First posted on arXiv 26 Feb 2026.

## Builds on (most cited first, 22 of 22)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Events-to-Video: Bringing Modern Computer Vision to Event Cameras](https://mlsanity.com/paper/1904.08298/) (2019)
- [Learning-Based View Synthesis for Light Field Cameras](https://mlsanity.com/paper/1609.02974/) (2016)
- [Back to Event Basics: Self-Supervised Learning of Image Reconstruction for Event Cameras via Photometric Constancy](https://mlsanity.com/paper/2009.08283/) (2020)
- [Time Lens++: Event-based Frame Interpolation with Parametric Non-linear Flow and Multi-scale Fusion](https://mlsanity.com/paper/2203.17191/) (2022)
- [EventNeRF: Neural Radiance Fields from a Single Colour Event Camera](https://mlsanity.com/paper/2206.11896/) (2022)
- [EPINET: A Fully-Convolutional Neural Network Using Epipolar Geometry for Depth from Light Field Images](https://mlsanity.com/paper/1804.02379/) (2018)
- [Multi-Bracket High Dynamic Range Imaging with Event Cameras](https://mlsanity.com/paper/2203.06622/) (2022)
- [HyperE2VID: Improving Event-Based Video Reconstruction via Hypernetworks](https://mlsanity.com/paper/2305.06382/) (2023)
- [A 4D Light-Field Dataset and CNN Architectures for Material Recognition](https://mlsanity.com/paper/1608.06985/) (2016)
- [EVREAL: Towards a Comprehensive Benchmark and Analysis Suite for Event-based Video Reconstruction](https://mlsanity.com/paper/2305.00434/) (2023)
- [DeepBinaryMask: Learning a Binary Mask for Video Compressive Sensing](https://mlsanity.com/paper/1607.03343/) (2016)
- [Time-Multiplexed Coded Aperture Imaging: Learned Coded Aperture and Pixel Exposures for Compressive Imaging Systems](https://mlsanity.com/paper/2104.02820/) (2021)
- [EventLFM: Event Camera integrated Fourier Light Field Microscopy for Ultrafast 3D imaging](https://mlsanity.com/paper/2310.00730/) (2023)
- [Temporal-Mapping Photography for Event Cameras](https://mlsanity.com/paper/2403.06443/) (2024)
- [Time-Efficient Light-Field Acquisition Using Coded Aperture and Events](https://mlsanity.com/paper/2403.07244/) (2024)
- [Deep Optics for Video Snapshot Compressive Imaging](https://mlsanity.com/paper/2404.05274/) (2024)
- [A Unified Learning Based Framework for Light Field Reconstruction from Coded Projections](https://mlsanity.com/paper/1812.10532/) (2018)
- [Edge-aware Bidirectional Diffusion for Dense Depth Estimation from Light Fields](https://mlsanity.com/paper/2107.02967/) (2021)
- [Acquiring a Dynamic Light Field through a Single-Shot Coded Image](https://mlsanity.com/paper/2204.12089/) (2022)
- [Event fields: Capturing light fields at high speed, resolution, and dynamic range](https://mlsanity.com/paper/2412.06191/) (2024)
- [Fast and Accurate Reconstruction of Compressed Color Light Field](https://mlsanity.com/paper/1801.10351/) (2018)

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

- [Modelling dynamic systems transfer functions from events in computational neuromorphic imaging](https://mlsanity.com/paper/2609.29863/) (2026)

## Abstract (arXiv)

We propose Coded-E2LF (coded event to light field), a computational imaging method for acquiring a 4-D light field using a coded aperture and a stationary event-only camera. In a previous work, an imaging system similar to ours was adopted, but both events and intensity images were captured and used for light field reconstruction. In contrast, our method is purely event-based, which relaxes restrictions for hardware implementation. We also introduce several advancements from the previous work that enable us to theoretically support and practically improve light field reconstruction from events alone. In particular, we clarify the key role of a black pattern in aperture coding patterns. We finally implemented our method on real imaging hardware to demonstrate its effectiveness in capturing real 3-D scenes. To the best of our knowledge, we are the first to demonstrate that a 4-D light field with pixel-level accuracy can be reconstructed from events alone. Our software and supplementary video are available from our project website.