基于率失真优化的高效动态NeRF体积视频编码
Efficient Dynamic-NeRF Based Volumetric Video Coding with Rate Distortion Optimization
AI总结：
本文提出一种基于动态NeRF的体积视频压缩方法，通过分解表示并联合优化建模与压缩，相比ReRF实现了更高压缩效率。
摘要：
体积视频凭借沉浸式3D真实感和交互性，在各种应用中具有巨大潜力，但巨大的数据量对压缩提出了重大挑战。近年来，NeRF凭借其简单的表示和强大的3D建模能力，在体积视频压缩中展现出显著潜力，其中一项代表性工作是ReRF。然而，ReRF将建模与压缩过程分离，导致压缩效率次优。相比之下，本文提出了一种基于动态NeRF的体积视频压缩方法，以更紧凑的方式进行。具体而言，我们将NeRF表示分解为系数场和基场，在时间域上增量更新基场以实现动态建模。此外，我们对建模和压缩过程进行端到端联合优化，以进一步提高压缩效率。大量实验表明，与ReRF相比，我们的方法在各种数据集上实现了更高的压缩效率。
英文摘要：
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.
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