Efficient Dynamic-NeRF Based Volumetric Video Coding with Rate
  Distortion Optimization
Zhiyu Zhang Guo Lu Huanxiong Liang Anni Tang Qiang Hu Li Song
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.