Neural Radiance Fields (NeRFs) have emerged as powerful tools for capturing
detailed 3D scenes through continuous volumetric representations. Recent NeRFs
utilize feature grids to improve rendering quality and speed; however, these
representations introduce significant storage overhead. This paper presents a
novel method for efficiently compressing a grid-based NeRF model, addressing
the storage overhead concern. Our approach is based on the non-linear transform
coding paradigm, employing neural compression for compressing the model's
feature grids. Due to the lack of training data involving many i.i.d scenes, we
design an encoder-free, end-to-end optimized approach for individual scenes,
using lightweight decoders. To leverage the spatial inhomogeneity of the latent
feature grids, we introduce an importance-weighted rate-distortion objective
and a sparse entropy model employing a masking mechanism. Our experimental
results validate that our proposed method surpasses existing works in terms of
grid-based NeRF compression efficacy and reconstruction quality.

该论文提出了一种针对基于网格的 NeRF 模型的效率压缩方法，使用非线性变换编码范式和神经压缩对模型的特征网格进行压缩，引入重要性加权的失真率目标和利用掩蔽机制的稀疏熵模型以利用潜在特征网格的空间不均匀性，实验证明该方法在基于网格的 NeRF 压缩效率和重建质量方面优于现有工作。