Park In Kyu, Lee Kyoung Mu
IEEE Trans Pattern Anal Mach Intell. 2018 Oct;40(10):2484-2497. doi: 10.1109/TPAMI.2017.2746858. Epub 2017 Aug 31.
Depth estimation is essential in many light field applications. Numerous algorithms have been developed using a range of light field properties. However, conventional data costs fail when handling noisy scenes in which occlusion is present. To address this problem, we introduce a light field depth estimation method that is more robust against occlusion and less sensitive to noise. Two novel data costs are proposed, which are measured using the angular patch and refocus image, respectively. The constrained angular entropy cost (CAE) reduces the effects of the dominant occluder and noise in the angular patch, resulting in a low cost. The constrained adaptive defocus cost (CAD) provides a low cost in the occlusion region, while also maintaining robustness against noise. Integrating the two data costs is shown to significantly improve the occlusion and noise invariant capability. Cost volume filtering and graph cut optimization are applied to improve the accuracy of the depth map. Our experimental results confirm the robustness of the proposed method and demonstrate its ability to produce high-quality depth maps from a range of scenes. The proposed method outperforms other state-of-the-art light field depth estimation methods in both qualitative and quantitative evaluations.
深度估计在许多光场应用中至关重要。人们已经利用一系列光场特性开发了许多算法。然而，传统的数据代价在处理存在遮挡的噪声场景时效果不佳。为了解决这个问题，我们引入了一种对遮挡更具鲁棒性且对噪声不太敏感的光场深度估计方法。我们提出了两种新颖的数据代价，分别使用角度块和重聚焦图像来度量。约束角度熵代价（CAE）减少了角度块中主要遮挡物和噪声的影响，从而得到较低的代价。约束自适应散焦代价（CAD）在遮挡区域提供较低的代价，同时还保持对噪声的鲁棒性。结果表明，将这两种数据代价相结合可显著提高遮挡和噪声不变能力。应用代价体滤波和图割优化来提高深度图的准确性。我们的实验结果证实了所提方法的鲁棒性，并展示了其从一系列场景中生成高质量深度图的能力。在所提方法在定性和定量评估中均优于其他当前最先进的光场深度估计方法。
