Neural Radiance Field (NeRF) excels in photo-realistically static scenes, inspiring numerous efforts to facilitate volumetric videos. However, rendering dynamic and long-sequence radiance fields remains challenging due to the significant data required to represent volumetric videos. In this paper, we propose a novel end-to-end joint optimization scheme of dynamic NeRF representation and compression, called JointRF, thus achieving significantly improved quality and compression efficiency against the previous methods. Specifically, JointRF employs a compact residual feature grid and a coefficient feature grid to represent the dynamic NeRF. This representation handles large motions without compromising quality while concurrently diminishing temporal redundancy. We also introduce a sequential feature compression subnetwork to further reduce spatial-temporal redundancy. Finally, the representation and compression subnetworks are end-to-end trained combined within the JointRF. Extensive experiments demonstrate that JointRF can achieve superior compression performance across various datasets.

翻译：神经辐射场（NeRF）在静态场景的光照真实感渲染方面表现优异，这启发了大量促进体视频技术发展的研究。然而，由于表示体视频所需数据量巨大，渲染动态且长序列的辐射场仍然具有挑战性。本文提出了一种新颖的动态NeRF表示与压缩端到端联合优化方案，称为JointRF，从而在重建质量与压缩效率上相比现有方法实现了显著提升。具体而言，JointRF采用紧凑的残差特征网格与系数特征网格来表示动态NeRF。该表示方法能够在保持质量的同时处理大幅运动，并同时减少时间冗余。我们还引入了序列特征压缩子网络以进一步降低时空冗余。最终，表示子网络与压缩子网络在JointRF框架内进行端到端联合训练。大量实验表明，JointRF在多种数据集上均能实现优异的压缩性能。