  Reconstructing neural radiance fields with explicit volumetric
representations, demonstrated by Plenoxels, has shown remarkable advantages on
training and rendering efficiency, while grid-based representations typically
induce considerable overhead for storage and transmission. In this work, we
present a simple and effective framework for pursuing compact radiance fields
from the perspective of compression methodology. By exploiting intrinsic
properties exhibiting in grid models, a non-uniform compression stem is
developed to significantly reduce model complexity and a novel parameterized
module, named Neural Codebook, is introduced for better encoding high-frequency
details specific to per-scene models via a fast optimization. Our approach can
achieve over 40 $\times$ reduction on grid model storage with competitive
rendering quality. In addition, the method can achieve real-time rendering
speed with 180 fps, realizing significant advantage on storage cost compared to
real-time rendering methods.
