Shi Jinglei, Jiang Xiaoran, Guillemot Christine
IEEE Trans Image Process. 2019 Dec;28(12):5867-5880. doi: 10.1109/TIP.2019.2923323. Epub 2019 Jun 21.
In this paper, we propose a learning-based depth estimation framework suitable for both densely and sparsely sampled light fields. The proposed framework consists of three processing steps: initial depth estimation, fusion with occlusion handling, and refinement. The estimation can be performed from a flexible subset of input views. The fusion of initial disparity estimates, relying on two warping error measures, allows us to have an accurate estimation in occluded regions and along the contours. In contrast with methods relying on the computation of cost volumes, the proposed approach does not need any prior information on the disparity range. Experimental results show that the proposed method outperforms state-of-the-art light fields depth estimation methods, including prior methods based on deep neural architectures.
在本文中，我们提出了一种基于学习的深度估计框架，适用于密集采样和稀疏采样的光场。所提出的框架包括三个处理步骤：初始深度估计、带遮挡处理的融合以及细化。该估计可以从输入视图的灵活子集中进行。基于两种扭曲误差度量的初始视差估计融合，使我们能够在遮挡区域和沿轮廓处进行准确估计。与依赖于成本体计算的方法相比，所提出的方法不需要关于视差范围的任何先验信息。实验结果表明，所提出的方法优于当前最先进的光场深度估计方法，包括基于深度神经架构的先前方法。
