We propose a learning-based approach to synthesize a light field with a small baseline from a single image. We synthesize the novel view images by first using a convolutional neural network (CNN) to promote the input image into a layered representation of the scene. We extend the multiplane image (MPI) representation by allowing the disparity of the layers to be inferred from the input image. We show that, compared to the original MPI representation, our representation models the scenes more accurately. Moreover, we propose to handle the visible and occluded regions separately through two parallel networks. The synthesized images using these two networks are then combined through a soft visibility mask to generate the final results. To effectively train the networks, we introduce a large-scale light field dataset of over 2,000 unique scenes containing a wide range of objects. We demonstrate that our approach synthesizes high-quality light fields on a variety of scenes, better than the state-of-the-art methods.

中文翻译：

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从具有可变 MPI 和两个网络融合的单个图像合成光场

我们提出了一种基于学习的方法来从单个图像合成具有小基线的光场。我们首先使用卷积神经网络 (CNN) 将输入图像提升为场景的分层表示，从而合成新颖的视图图像。我们通过允许从输入图像推断层的差异来扩展多平面图像（MPI）表示。我们表明，与原始 MPI 表示相比，我们的表示更准确地模拟了场景。此外，我们建议通过两个并行网络分别处理可见区域和遮挡区域。使用这两个网络的合成图像然后通过软可见性掩码组合以生成最终结果。为了有效地训练网络，我们引入了一个超过 2 的大规模光场数据集，000 个包含各种对象的独特场景。我们证明了我们的方法在各种场景中合成了高质量的光场，比最先进的方法更好。