Accurate Light Field Depth Estimation with Superpixel Regularization over Partially Occluded Regions
This work addresses a specific challenge in light field depth estimation for applications like photography, but it is incremental as it builds on existing methods to improve occlusion handling.
The paper tackles the problem of depth estimation in light field photography, particularly in handling intricate occlusions, by proposing a framework that regularizes label confidence maps and edge strength weights over partially occluded regions, resulting in improved average disparity error rate and occlusion boundary precision-recall compared to state-of-the-art methods.
Depth estimation is a fundamental problem for light field photography applications. Numerous methods have been proposed in recent years, which either focus on crafting cost terms for more robust matching, or on analyzing the geometry of scene structures embedded in the epipolar-plane images. Significant improvements have been made in terms of overall depth estimation error; however, current state-of-the-art methods still show limitations in handling intricate occluding structures and complex scenes with multiple occlusions. To address these challenging issues, we propose a very effective depth estimation framework which focuses on regularizing the initial label confidence map and edge strength weights. Specifically, we first detect partially occluded boundary regions (POBR) via superpixel based regularization. Series of shrinkage/reinforcement operations are then applied on the label confidence map and edge strength weights over the POBR. We show that after weight manipulations, even a low-complexity weighted least squares model can produce much better depth estimation than state-of-the-art methods in terms of average disparity error rate, occlusion boundary precision-recall rate, and the preservation of intricate visual features.