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

TL;DR

This paper introduces a novel occlusion-model guided approach for depth estimation in light fields, effectively handling multi-occluder scenarios by leveraging occluder-consistency and an anti-occlusion energy function.

Contribution

It models multi-occluder occlusion in light fields and derives occluder-consistency to improve depth estimation accuracy in complex occlusion regions.

Findings

Outperforms state-of-the-art algorithms in multi-occluder areas

Demonstrates robustness in complex occlusion scenarios

Achieves higher accuracy on public datasets

Abstract

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.