Occlusion-Model Guided Anti-Occlusion Depth Estimation in Light Field
This addresses a specific challenge in computer vision for applications like 3D reconstruction, but it is incremental as it builds on prior single-occluder models.
The paper tackled the problem of multi-occluder occlusion in light field depth estimation by deriving an occluder-consistency model to guide view selection and building an anti-occlusion energy function, resulting in improved performance over state-of-the-art methods, especially in multi-occluder areas.
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.