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Occlusion-aware light field depth estimation using side window angular coherence
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AI
DOI：10.1364/ao.411070.png)
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Abstract
En 中文
Depth estimation is crucial in many light field applications. However, the accuracy of light field depth estimation is prone to be affected by occlusions. In this paper, a method of side window angular coherence is proposed to handle different types of occlusions, and the ability of the proposed method to resist occlusions is theoretically analyzed. The angular patch is divided into several discrete side window subsets. These subsets are a pure occluder-type subset, a pure object point–type subset, and a hybrid-type subset. The photo-consistency of the pure object point–type subset can reflect the true depth. Meanwhile, the occlusion edges can be detected to identify occluded points and nonoccluded points so the robustness of the algorithm can be further enhanced by processing the two types of points. Moreover, fast guided filtering is applied to cost volume for improving the accuracy of depth estimation. Experimental results demonstrate that our method outperforms the state-of-the-art depth estimation methods on both synthetic and real scenes, especially near occlusion boundaries.
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