Zhou Wenhui, Zhoua Enci, Liu Gaomin, Lin Lili, Lumsdaine Andrew
IEEE Trans Image Process. 2019 Oct 3. doi: 10.1109/TIP.2019.2944343.
Learning based depth estimation from light field has made significant progresses in recent years. However, most existing approaches are under the supervised framework, which requires vast quantities of ground-truth depth data for training. Furthermore, accurate depth maps of light field are hardly available except for a few synthetic datasets. In this paper, we exploit the multi-orientation epipolar geometry of light field and propose an unsupervised monocular depth estimation network. It predicts depth from the central view of light field without any ground-truth information. Inspired by the inherent depth cues and geometry constraints of light field, we then introduce three novel unsupervised loss functions: photometric loss, defocus loss and symmetry loss. We have evaluated our method on a public 4D light field synthetic dataset. As the first unsupervised method published in the 4D Light Field Benchmark website, our method can achieve satisfactory performance in most error metrics. Comparison experiments with two state-of-the-art unsupervised methods demonstrate the superiority of our method. We also prove the effectiveness and generality of our method on real-world light-field images.
近年来，基于学习的光场深度估计取得了显著进展。然而，大多数现有方法都处于监督框架之下，这需要大量的地面真值深度数据进行训练。此外，除了少数合成数据集外，很难获得光场的精确深度图。在本文中，我们利用光场的多视角外极几何，提出了一种无监督单目深度估计网络。它从光场的中心视图预测深度，无需任何地面真值信息。受光场固有的深度线索和几何约束的启发，我们引入了三种新颖的无监督损失函数：光度损失、散焦损失和对称损失。我们在一个公开的4D光场合成数据集上评估了我们的方法。作为在4D光场基准网站上发表的第一种无监督方法，我们的方法在大多数误差指标上都能取得令人满意的性能。与两种最新的无监督方法的对比实验证明了我们方法的优越性。我们还证明了我们的方法在真实光场图像上的有效性和通用性。
