  We propose a method to compute depth maps for every sub-aperture image in a
light field in a view consistent way. Previous light field depth estimation
methods typically estimate a depth map only for the central sub-aperture view,
and struggle with view consistent estimation. Our method precisely defines
depth edges via EPIs, then we diffuse these edges spatially within the central
view. These depth estimates are then propagated to all other views in an
occlusion-aware way. Finally, disoccluded regions are completed by diffusion in
EPI space. Our method runs efficiently with respect to both other classical and
deep learning-based approaches, and achieves competitive quantitative metrics
and qualitative performance on both synthetic and real-world light fields
