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

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.

19 pages, 13 figures, pdflatex

References in corpus (1)

• Learning-Based View Synthesis for Light Field Cameras

Cited by in corpus (6)

• Accurate Light Field Depth Estimation with Superpixel Regularization over Partially Occluded Regions
• Occlusion-aware Unsupervised Learning of Depth from 4-D Light Fields
• Depth Map Estimation for Free-Viewpoint Television
• Light Field Spatial Super-resolution via Deep Combinatorial Geometry Embedding and Structural Consistency Regularization
• Matching entropy based disparity estimation from light field
• Deep Selective Combinatorial Embedding and Consistency Regularization for Light Field Super-resolution