  Light fields are a type of image data that capture both spatial and angular
scene information by recording light rays emitted by a scene from different
orientations. In this context, spatial information is defined as features that
remain static regardless of perspectives, while angular information refers to
features that vary between viewpoints. We propose a novel neural network that,
by design, can separate angular and spatial information of a light field. The
network represents spatial information using spatial kernels shared among all
Sub-Aperture Images (SAIs), and angular information using sets of angular
kernels for each SAI. To further improve the representation capability of the
network without increasing parameter number, we also introduce angular kernel
allocation and kernel tensor decomposition mechanisms. Extensive experiments
demonstrate the benefits of information separation: when applied to the
compression task, our network outperforms other state-of-the-art methods by a
large margin. And angular information can be easily transferred to other scenes
for rendering dense views, showing the successful separation and the potential
use case for the view synthesis task. We plan to release the code upon
acceptance of the paper to encourage further research on this topic.
