Abstract

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.

Results on Realistic Synthetic 360^o Dataset.

chair (0%, 30%, 50%, 70% pruning; left to right)