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.

翻译：通过显式体积表示重构神经辐射场的方法（如Plenoxels所示）在训练与渲染效率上展现出显著优势，但基于网格的表示通常会导致存储与传输的巨大开销。本文从压缩方法学角度出发，提出一种简洁高效的紧凑型辐射场构建框架。通过挖掘网格模型中固有的内在属性，开发了一种非均匀压缩主干网络以大幅降低模型复杂度，并引入名为神经码本的新型参数化模块，通过快速优化实现场景特异性模型的高频细节高效编码。本方法在保持竞争性渲染质量的同时，可将网格模型存储量降低40倍以上。此外，该方法可实现180帧/秒的实时渲染速度，在存储成本上较现有实时渲染方法具有显著优势。