论文标题
使用多平面图像学习光场综合：场景编码为重复分割任务
Learning light field synthesis with Multi-Plane Images: scene encoding as a recurrent segmentation task
论文作者
论文摘要
在本文中，我们通过将一组稀疏的输入视图转换为多平面图像（MPI），从大基线光场中解决了综合问题。由于可用的数据集很少，因此我们提出了一个轻量级网络，不需要大量的培训。与最新的方法不同，我们的模型无法学会估计RGB层，而仅编码MPI Alpha层中的场景几何形状，这归结为细分任务。学习的梯度下降（LGD）框架用于以复发方式级联相同的卷积网络，以完善所获得的体积表示。由于其参数数量少，我们的模型在小型光场视频数据集上成功训练，并提供了视觉吸引力的结果。它还表现出有关输入视图数量，MPI中的深度平面数和细化迭代次数的方便概括属性。
In this paper we address the problem of view synthesis from large baseline light fields, by turning a sparse set of input views into a Multi-plane Image (MPI). Because available datasets are scarce, we propose a lightweight network that does not require extensive training. Unlike latest approaches, our model does not learn to estimate RGB layers but only encodes the scene geometry within MPI alpha layers, which comes down to a segmentation task. A Learned Gradient Descent (LGD) framework is used to cascade the same convolutional network in a recurrent fashion in order to refine the volumetric representation obtained. Thanks to its low number of parameters, our model trains successfully on a small light field video dataset and provides visually appealing results. It also exhibits convenient generalization properties regarding both the number of input views, the number of depth planes in the MPI, and the number of refinement iterations.
