Grid Based NeRF

Grid-based Neural Radiance Fields (NeRFs) represent 3D scenes as volumetric grids, offering faster rendering and improved quality compared to earlier NeRF approaches. Current research focuses on optimizing these grids for efficient storage and compression, enhancing their generalizability across diverse scenes using techniques inspired by large language models (like Mixture-of-Experts), and developing methods for faster training with flexible camera trajectories. These advancements are significant because they address key limitations of traditional NeRFs, paving the way for real-time applications in areas such as virtual and augmented reality, 3D modeling, and robotics.