Light field depth estimation using occlusion-aware consistency analysis

Occlusion modeling is critical for light field depth estimation, since occlusion destroys the photo-consistency assumption, which most depth estimation methods hold. Previous works always detect the occlusion points on the basis of Canny detector, which can leave some occlusion points out. Occlusion handling, especially for multi-occluder occlusion, is still challenging. In this paper, we propose a novel occlusion-aware depth estimation method, which can better solve the occlusion problem. We design two novel consistency costs based on the photo-consistency for depth estimation. According to the consistency costs, we analyze the influence of the occlusion and propose an occlusion detection technique based on depth consistency, which can detect the occlusion points more accurately. For the occlusion point, we adopt a new data cost to select the un-occluded views, which are used to determine the depth. Experimental results demonstrate that the proposed method is superior to the other compared algorithms, especially in multi-occluder occlusions.