  Novel view synthesis (NVS) is a challenge in computer vision and graphics,
focusing on generating realistic images of a scene from unobserved camera
poses, given a limited set of authentic input images. Neural radiance fields
(NeRF) achieved impressive results in rendering quality by utilizing volumetric
rendering. However, NeRF and its variants are unsuitable for mobile devices due
to the high computational cost of volumetric rendering. Emerging research in
neural light fields (NeLF) eliminates the need for volumetric rendering by
directly learning a mapping from ray representation to pixel color. NeLF has
demonstrated its capability to achieve results similar to NeRF but requires a
more extensive, computationally intensive network that is not mobile-friendly.
Unlike existing works, this research builds upon the novel network architecture
introduced by MobileR2L and aggressively applies a compression technique
(channel-wise structure pruning) to produce a model that runs efficiently on
mobile devices with lower latency and smaller sizes, with a slight decrease in
performance.
