Efficient Neural Light Fields (ENeLF) for Mobile Devices
This work addresses the challenge of deploying high-quality view synthesis on resource-constrained mobile devices, representing an incremental improvement over existing mobile-friendly methods.
The paper tackles the problem of enabling neural light fields (NeLF) for novel view synthesis on mobile devices by compressing the network architecture to reduce computational cost and model size, achieving lower latency with a slight performance decrease.
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.