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.

提出了一种基于动态 NeRF 的体积视频压缩方法，通过将 NeRF 表示分解为系数场和基准场并在时间域内增量更新基准场来实现动态建模，同时进行端到端联合优化以进一步提高压缩效率。大量实验证明，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。

高效动态 NeRF 基于体积视频编码与速率失真优化

Efficient Dynamic-NeRF Based Volumetric Video Coding with Rate  Distortion Optimization

This paper introduces a novel representation of volumetric videos for
real-time view synthesis of dynamic scenes. Recent advances in neural scene
representations demonstrate their remarkable capability to model and render
complex static scenes, but extending them to represent dynamic scenes is not
straightforward due to their slow rendering speed or high storage cost. To
solve this problem, our key idea is to represent the radiance field of each
frame as a set of shallow MLP networks whose parameters are stored in 2D grids,
called MLP maps, and dynamically predicted by a 2D CNN decoder shared by all
frames. Representing 3D scenes with shallow MLPs significantly improves the
rendering speed, while dynamically predicting MLP parameters with a shared 2D
CNN instead of explicitly storing them leads to low storage cost. Experiments
show that the proposed approach achieves state-of-the-art rendering quality on
the NHR and ZJU-MoCap datasets, while being efficient for real-time rendering
with a speed of 41.7 fps for $512 \times 512$ images on an RTX 3090 GPU. The
code is available at this https URL

本文提出了一种用于动态场景实时视角合成的体积视频新表示法，通过使用浅层 MLP 网格和共享的 2D CNN 解码器动态预测 MLP 参数，显著提高了渲染速度并降低了存储成本，实验表明，该方法在渲染质量上实现了最先进的水平，同时能够通过 RTX 3090 GPU 以 41.7fps 的速度对 $512 	imes 512$ 的图像实时渲染。

用动态 MLP 地图表示立体视频

Representing Volumetric Videos as Dynamic MLP Maps

Recent years have witnessed a rapid development of immersive multimedia which
bridges the gap between the real world and virtual space. Volumetric videos, as
an emerging representative 3D video paradigm that empowers extended reality,
stand out to provide unprecedented immersive and interactive video watching
experience. Despite the tremendous potential, the research towards 3D
volumetric video is still in its infancy, relying on sufficient and complete
datasets for further exploration. However, existing related volumetric video
datasets mostly only include a single object, lacking details about the scene
and the interaction between them. In this paper, we focus on the current most
widely used data format, point cloud, and for the first time release a
full-scene volumetric video dataset that includes multiple people and their
daily activities interacting with the external environments. Comprehensive
dataset description and analysis are conducted, with potential usage of this
dataset. The dataset and additional tools can be accessed via the following
website: this https URL

本文描述和分析了第一个包含多个人和场景互动的全景体数据集并附加数据工具