Volumetric videos, benefiting from immersive 3D realism and interactivity, hold vast potential for various applications, while the tremendous data volume poses significant challenges for compression. Recently, NeRF has demonstrated remarkable potential in volumetric video compression thanks to its simple representation and powerful 3D modeling capabilities, where a notable work is ReRF. However, ReRF separates the modeling from compression process, resulting in suboptimal compression efficiency. In contrast, in this paper, we propose a volumetric video compression method based on dynamic NeRF in a more compact manner. Specifically, we decompose the NeRF representation into the coefficient fields and the basis fields, incrementally updating the basis fields in the temporal domain to achieve dynamic modeling. Additionally, we perform end-to-end joint optimization on the modeling and compression process to further improve the compression efficiency. Extensive experiments demonstrate that our method achieves higher compression efficiency compared to ReRF on various datasets.

翻译：体积视频凭借其沉浸式的三维真实感与交互性，在众多应用领域展现出巨大潜力，然而其庞大的数据量也给压缩带来了显著挑战。近年来，NeRF凭借其简洁的表示形式和强大的三维建模能力，在体积视频压缩中展现出卓越潜力，其中ReRF是一项代表性工作。然而，ReRF将建模过程与压缩过程分离，导致压缩效率未能达到最优。相比之下，本文提出了一种基于动态NeRF的、更为紧凑的体积视频压缩方法。具体而言，我们将NeRF表示分解为系数场与基场，通过在时域内增量更新基场来实现动态建模。此外，我们对建模与压缩过程进行端到端的联合优化，以进一步提升压缩效率。大量实验表明，与ReRF相比，本方法在多个数据集上均实现了更高的压缩效率。