# EPINET: A Fully-Convolutional Neural Network Using Epipolar Geometry for Depth from Light Field Images

arXiv: https://arxiv.org/abs/1804.02379 · HTML: https://mlsanity.com/paper/1804.02379/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2018.

## Built on by (most cited first, 30 of 46)

- [Disentangling Light Fields for Super-Resolution and Disparity Estimation](https://mlsanity.com/paper/2202.10603/) (2022)
- [Occlusion-Aware Cost Constructor for Light Field Depth Estimation](https://mlsanity.com/paper/2203.01576/) (2022)
- [Occlusion-aware Unsupervised Learning of Depth from 4-D Light Fields](https://mlsanity.com/paper/2106.03043/) (2021)
- [Learning Sub-Pixel Disparity Distribution for Light Field Depth Estimation](https://mlsanity.com/paper/2208.09688/) (2022)
- [Time-Efficient Light-Field Acquisition Using Coded Aperture and Events](https://mlsanity.com/paper/2403.07244/) (2024)
- [Real-World Light Field Image Super-Resolution via Degradation Modulation](https://mlsanity.com/paper/2206.06214/) (2022)
- [Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile Vision](https://mlsanity.com/paper/2109.08880/) (2021)
- [Dense Dual-Attention Network for Light Field Image Super-Resolution](https://mlsanity.com/paper/2110.12114/) (2021)
- [OPAL: Occlusion Pattern Aware Loss for Unsupervised Light Field Disparity Estimation](https://mlsanity.com/paper/2203.02231/) (2022)
- [NTIRE 2023 Challenge on Light Field Image Super-Resolution: Dataset, Methods and Results](https://mlsanity.com/paper/2304.10415/) (2023)
- [Probabilistic-based Feature Embedding of 4-D Light Fields for Compressive Imaging and Denoising](https://mlsanity.com/paper/2306.08836/) (2023)
- [Edge-aware Bidirectional Diffusion for Dense Depth Estimation from Light Fields](https://mlsanity.com/paper/2107.02967/) (2021)
- [LFSRDiff: Light Field Image Super-Resolution via Diffusion Models](https://mlsanity.com/paper/2311.16517/) (2023)
- [Differentiable Diffusion for Dense Depth Estimation from Multi-view Images](https://mlsanity.com/paper/2106.08917/) (2021)
- [Light Field Depth Estimation via Stitched Epipolar Plane Images](https://mlsanity.com/paper/2203.15201/) (2022)
- [Acquiring a Dynamic Light Field through a Single-Shot Coded Image](https://mlsanity.com/paper/2204.12089/) (2022)
- [Unsupervised Learning of Depth Estimation and Visual Odometry for Sparse Light Field Cameras](https://mlsanity.com/paper/2103.11322/) (2021)
- [Learning Dynamic Interpolation for Extremely Sparse Light Fields with Wide Baselines](https://mlsanity.com/paper/2108.07408/) (2021)
- [Texture-enhanced Light Field Super-resolution with Spatio-Angular Decomposition Kernels](https://mlsanity.com/paper/2111.04069/) (2021)
- [Unsupervised Light Field Depth Estimation via Multi-view Feature Matching with Occlusion Prediction](https://mlsanity.com/paper/2301.08433/) (2023)
- [OccCasNet: Occlusion-aware Cascade Cost Volume for Light Field Depth Estimation](https://mlsanity.com/paper/2305.17710/) (2023)
- [Joint 3D Shape and Motion Estimation from Rolling Shutter Light-Field Images](https://mlsanity.com/paper/2311.01292/) (2023)
- [Scale-Consistent Fusion: from Heterogeneous Local Sampling to Global Immersive Rendering](https://mlsanity.com/paper/2106.09548/) (2021)
- [Efficient Light Field Reconstruction via Spatio-Angular Dense Network](https://mlsanity.com/paper/2108.03635/) (2021)
- [Iterative approach to reconstructing neural disparity fields from light-field data](https://mlsanity.com/paper/2407.15380/) (2024)
- [LGFN: Lightweight Light Field Image Super-Resolution using Local Convolution Modulation and Global Attention Feature Extraction](https://mlsanity.com/paper/2409.17759/) (2024)
- [Spectral Reconstruction and Disparity from Spatio-Spectrally Coded Light Fields via Multi-Task Deep Learning](https://mlsanity.com/paper/2103.10179/) (2021)
- [Dynamic Fusion Network For Light Field Depth Estimation](https://mlsanity.com/paper/2104.05969/) (2021)
- [Learning Multi-modal Information for Robust Light Field Depth Estimation](https://mlsanity.com/paper/2104.05971/) (2021)
- [Matching entropy based disparity estimation from light field](https://mlsanity.com/paper/2210.15948/) (2022)

## Abstract (arXiv)

Light field cameras capture both the spatial and the angular properties of light rays in space. Due to its property, one can compute the depth from light fields in uncontrolled lighting environments, which is a big advantage over active sensing devices. Depth computed from light fields can be used for many applications including 3D modelling and refocusing. However, light field images from hand-held cameras have very narrow baselines with noise, making the depth estimation difficult. any approaches have been proposed to overcome these limitations for the light field depth estimation, but there is a clear trade-off between the accuracy and the speed in these methods. In this paper, we introduce a fast and accurate light field depth estimation method based on a fully-convolutional neural network. Our network is designed by considering the light field geometry and we also overcome the lack of training data by proposing light field specific data augmentation methods. We achieved the top rank in the HCI 4D Light Field Benchmark on most metrics, and we also demonstrate the effectiveness of the proposed method on real-world light-field images.