  Since the introduction of NeRFs, considerable attention has been focused on
improving their training and inference times, leading to the development of
Fast-NeRFs models. Despite demonstrating impressive rendering speed and
quality, the rapid convergence of such models poses challenges for further
improving reconstruction quality. Common strategies to improve rendering
quality involves augmenting model parameters or increasing the number of
sampled points. However, these computationally intensive approaches encounter
limitations in achieving significant quality enhancements. This study
introduces a model-agnostic framework inspired by Sparsely-Gated Mixture of
Experts to enhance rendering quality without escalating computational
complexity. Our approach enables specialization in rendering different scene
components by employing a mixture of experts with varying resolutions. We
present a novel gate formulation designed to maximize expert capabilities and
propose a resolution-based routing technique to effectively induce sparsity and
decompose scenes. Our work significantly improves reconstruction quality while
maintaining competitive performance.
