# Light Field Depth Estimation via Stitched Epipolar Plane Images

arXiv: https://arxiv.org/abs/2203.15201 · HTML: https://mlsanity.com/paper/2203.15201/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 74 references that are in the map.
- First posted on arXiv 29 Mar 2022.

## Builds on (most cited first, 11 of 11)

- [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)
- [Accurate Light Field Depth Estimation with Superpixel Regularization over Partially Occluded Regions](https://mlsanity.com/paper/1708.01964/) (2017)
- [Light Field Reconstruction Using Convolutional Network on EPI and Extended Applications](https://mlsanity.com/paper/2103.13043/) (2021)
- [Occlusion-aware Unsupervised Learning of Depth from 4-D Light Fields](https://mlsanity.com/paper/2106.03043/) (2021)
- [Occlusion-Model Guided Anti-Occlusion Depth Estimation in Light Field](https://mlsanity.com/paper/1608.04187/) (2016)
- [Fast and Accurate Depth Estimation from Sparse Light Fields](https://mlsanity.com/paper/1812.06856/) (2018)
- [Cross-Scale Cost Aggregation for Stereo Matching](https://mlsanity.com/paper/1403.0316/) (2014)
- [Hyperspectral Light Field Stereo Matching](https://mlsanity.com/paper/1709.00835/) (2017)
- [Edge-aware Bidirectional Diffusion for Dense Depth Estimation from Light Fields](https://mlsanity.com/paper/2107.02967/) (2021)
- [Differentiable Diffusion for Dense Depth Estimation from Multi-view Images](https://mlsanity.com/paper/2106.08917/) (2021)

## Built on by (most cited first, 4 of 4)

- [Enhancing Underwater Imaging with 4-D Light Fields: Dataset and Method](https://mlsanity.com/paper/2408.17339/) (2024)
- [Learning based Deep Disentangling Light Field Reconstruction and Disparity Estimation Application](https://mlsanity.com/paper/2311.08129/) (2023)
- [Self-supervised Learning-based Reconstruction of High-resolution 4D Light Fields](https://mlsanity.com/paper/2402.19020/) (2024)
- [Exploring Non-Local Spatial-Angular Correlations with a Hybrid Mamba-Transformer Framework for Light Field Super-Resolution](https://mlsanity.com/paper/2509.04824/) (2025)

## Abstract (arXiv)

Depth estimation is a fundamental problem in light field processing. Epipolar-plane image (EPI)-based methods often encounter challenges such as low accuracy in slope computation due to discretization errors and limited angular resolution. Besides, existing methods perform well in most regions but struggle to produce sharp edges in occluded regions and resolve ambiguities in texture-less regions. To address these issues, we propose the concept of stitched-EPI (SEPI) to enhance slope computation. SEPI achieves this by shifting and concatenating lines from different EPIs that correspond to the same 3D point. Moreover, we introduce the half-SEPI algorithm, which focuses exclusively on the non-occluded portion of lines to handle occlusion. Additionally, we present a depth propagation strategy aimed at improving depth estimation in texture-less regions. This strategy involves determining the depth of such regions by progressing from the edges towards the interior, prioritizing accurate regions over coarse regions. Through extensive experimental evaluations and ablation studies, we validate the effectiveness of our proposed method. The results demonstrate its superior ability to generate more accurate and robust depth maps across all regions compared to state-of-the-art methods. The source code will be publicly available at https://github.com/PingZhou-LF/Light-Field-Depth-Estimation-Based-on-Stitched-EPIs.