# Occlusion-Model Guided Anti-Occlusion Depth Estimation in Light Field

arXiv: https://arxiv.org/abs/1608.04187 · HTML: https://mlsanity.com/paper/1608.04187/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2016.

## Built on by (most cited first, 6 of 6)

- [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)
- [Light Field Depth Estimation via Stitched Epipolar Plane Images](https://mlsanity.com/paper/2203.15201/) (2022)
- [OccCasNet: Occlusion-aware Cascade Cost Volume for Light Field Depth Estimation](https://mlsanity.com/paper/2305.17710/) (2023)
- [Interactive Occlusion Boundary Estimation through Exploitation of Synthetic Data](https://mlsanity.com/paper/2408.15038/) (2024)
- [Matching entropy based disparity estimation from light field](https://mlsanity.com/paper/2210.15948/) (2022)

## Abstract (arXiv)

Occlusion is one of the most challenging problems in depth estimation. Previous work has modeled the single-occluder occlusion in light field and get good results, however it is still difficult to obtain accurate depth for multi-occluder occlusion. In this paper, we explore the multi-occluder occlusion model in light field, and derive the occluder-consistency between the spatial and angular space which is used as a guidance to select the un-occluded views for each candidate occlusion point. Then an anti-occlusion energy function is built to regularize depth map. The experimental results on public light field datasets have demonstrated the advantages of the proposed algorithm compared with other state-of-the-art light field depth estimation algorithms, especially in multi-occluder areas.