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A Novel Occlusion-aware Vote Cost for Light Field Depth Estimation.
IEEE Transactions on Pattern Analysis and Machine Intelligence  (IF16.389),  Pub Date : 2021-08-18, DOI: 10.1109/tpami.2021.3105523
Kang Han,Wei Xiang,Eric Wang,Tao Huang

Conventional light field depth estimation methods build a cost volume that measures the photo-consistency of pixels refocused to a range of depths, which works well in most regions but usually generates blurry edges in the estimated depth map due to occlusions. Existing occlusion handling methods rely on complex edge-aided processing and post-refinement, and this reliance limits the resultant depth accuracy and impacts on the computational performance. In this paper, we propose a novel occlusion-aware vote cost (OAVC) which is able to accurately preserve edges in the depth map. Instead of using photo-consistency as an indicator of the correct depth, we construct a novel cost from a new perspective that counts the number of refocused pixels whose deviations from the central-view pixel is less than a small threshold, and utilizes that number to select the correct depth. The pixels from occluders are thus excluded in determining the correct depth. Without the use of any explicit occlusion handling methods, the proposed method can inherently preserve edges and produces high-quality depth estimates. Experimental results show that the proposed OAVC outperforms state-of-the-art light field depth estimation methods in terms of depth estimation accuracy and the computational performance.