# Iterative Occlusion-Aware Light Field Depth Estimation using 4D Geometrical Cues

arXiv: https://arxiv.org/abs/2403.02043 · HTML: https://mlsanity.com/paper/2403.02043/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 65 references that are in the map.
- First posted on arXiv 4 Mar 2024.

## Builds on (most cited first, 7 of 7)

- [EPINET: A Fully-Convolutional Neural Network Using Epipolar Geometry for Depth from Light Field Images](https://mlsanity.com/paper/1804.02379/) (2018)
- [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)
- [EPI-based Oriented Relation Networks for Light Field Depth Estimation](https://mlsanity.com/paper/2007.04538/) (2020)
- [Edge-aware Bidirectional Diffusion for Dense Depth Estimation from Light Fields](https://mlsanity.com/paper/2107.02967/) (2021)
- [Unsupervised Light Field Depth Estimation via Multi-view Feature Matching with Occlusion Prediction](https://mlsanity.com/paper/2301.08433/) (2023)

## Abstract (arXiv)

Light field cameras and multi-camera arrays have emerged as promising solutions for accurately estimating depth by passively capturing light information. This is possible because the 3D information of a scene is embedded in the 4D light field geometry. Commonly, depth estimation methods extract this information relying on gradient information, heuristic-based optimisation models, or learning-based approaches. This paper focuses mainly on explicitly understanding and exploiting 4D geometrical cues for light field depth estimation. Thus, a novel method is proposed, based on a non-learning-based optimisation approach for depth estimation that explicitly considers surface normal accuracy and occlusion regions by utilising a fully explainable 4D geometric model of the light field. The 4D model performs depth/disparity estimation by determining the orientations and analysing the intersections of key 2D planes in 4D space, which are the images of 3D-space points in the 4D light field. Experimental results show that the proposed method outperforms both learning-based and non-learning-based state-of-the-art methods in terms of surface normal angle accuracy, achieving a Median Angle Error on planar surfaces, on average, 26.3% lower than the state-of-the-art, and still being competitive with state-of-the-art methods in terms of MSE × 100 and Badpix 0.07.