Real-time novel-view image synthesis on mobile devices is prohibitive due to the limited computational power and storage. Using volumetric rendering methods, such as NeRF and its derivatives, on mobile devices is not suitable due to the high computational cost of volumetric rendering. On the other hand, recent advances in neural light field representations have shown promising real-time view synthesis results on mobile devices. Neural light field methods learn a direct mapping from a ray representation to the pixel color. The current choice of ray representation is either stratified ray sampling or Pl\"{u}cker coordinates, overlooking the classic light slab (two-plane) representation, the preferred representation to interpolate between light field views. In this work, we find that using the light slab representation is an efficient representation for learning a neural light field. More importantly, it is a lower-dimensional ray representation enabling us to learn the 4D ray space using feature grids which are significantly faster to train and render. Although mostly designed for frontal views, we show that the light-slab representation can be further extended to non-frontal scenes using a divide-and-conquer strategy. Our method offers superior rendering quality compared to previous light field methods and achieves a significantly improved trade-off between rendering quality and speed.

翻译：在移动设备上实现实时新视角图像合成因计算能力和存储空间受限而极具挑战性。由于体渲染的高计算成本，NeRF及其衍生方法等体渲染技术不适用于移动设备。另一方面，神经光场表示的最新进展已在移动设备上展现出有前景的实时视角合成效果。神经光场方法学习从光线表示到像素颜色的直接映射。当前光线表示的选择要么是分层光线采样，要么是普吕克坐标，忽略了经典的光板（双平面）表示——这种表示正是光场视角插值的首选方案。本研究发现，采用光板表示是学习神经光场的一种高效表示方式。更重要的是，这种低维光线表示使我们能够利用特征网格学习四维光线空间，从而显著加快训练和渲染速度。尽管光板表示主要针对正面视角设计，但我们展示了通过分治策略可将其进一步扩展至非正面场景。与以往光场方法相比，我们的方法具有更优的渲染质量，并在渲染质量与速度之间实现了显著更优的权衡。