Compact Real-time Radiance Fields with Neural Codebook
Lingzhi Li Zhongshu Wang Zhen Shen Li Shen Ping Tan
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.