CAGS：基于动态3D高斯点云的色彩自适应体视频流媒体
CAGS: Color-Adaptive Volumetric Video Streaming with Dynamic 3D Gaussian Splatting
AI总结：
本文提出CAGS系统，通过向量量化建立LOD并利用低分辨率参考图像校正颜色失真，提升体视频流媒体在带宽波动下的PSNR性能和渲染效率。
摘要：
体视频（VV）流媒体使远程3D环境的实时沉浸访问成为可能，推动了远程存在、生态监测和机器人远程操作等应用。这些应用将VV流媒体转化为远程物理环境的实时接口，对逼真场景表示、低延迟交互和异构网络下的鲁棒性能提出了新要求。3D高斯点云（3DGS）已被广泛用于实时逼真渲染，提供优异的视觉质量和渲染性能，但面临带宽消耗挑战。此外，作为自适应VV流媒体基础的现有层次细节（LoD）方法基于密度，不适用于高斯表示，导致可见间隙和严重质量下降。最近研究也探索了属性压缩技术以减少带宽消耗。我们的初步研究发现，激进的属性压缩主要导致颜色失真，可通过参考图像在渲染图像中有效校正。受这些发现启发，我们提出了一种新的色彩自适应方案，用于自适应VV流媒体，利用向量量化（VQ）建立LOD并利用低分辨率参考图像校正颜色失真。我们进一步提出CAGS，一种兼容多种高斯表示的自适应VV流媒体系统，通过在流媒体服务器渲染参考图像并在客户端执行颜色恢复来整合色彩自适应方案。在我们的原型系统上进行的广泛实验表明，CAGS在带宽波动下比现有自适应流媒体系统在PSNR上高出5~20 dB，其运行速度显著快于现有可扩展高斯压缩方法，并能跨不同高斯表示泛化。
英文摘要：
Volumetric video (VV) streaming enables real-time, immersive access to remote 3D environments, powering telepresence, ecological monitoring, and robotic teleoperation. These applications turn VV streaming into a real-time interface to remote physical environments, imposing new system-level demands for photorealistic scene representation, low-latency interaction, and robust performance under heterogeneous networks. 3D Gaussian Splatting (3DGS) has been widely used for real-time photorealistic rendering, offering superior visual quality and rendering performance, but it faces challenges due to bandwidth consumption. Furthermore, as the foundation of adaptive VV streaming, existing Levels of Detail (LoD) methods based on density are not well-suited to Gaussian representations, leading to visible gaps and severe quality degradation. Recent studies have also explored attribute compression techniques to reduce bandwidth consumption. Our preliminary studies reveal that aggressive attribute compression primarily causes color distortion, which can be effectively corrected in the rendered image using a reference image. Motivated by these findings, we propose a novel Color-Adaptive scheme for adaptive VV streaming that uses vector quantization (VQ) to establish LoDs and correct color distortions with low-resolution reference images. We further present CAGS, an adaptive VV streaming system compatible with diverse Gaussian representations, which integrates the Color-Adaptive scheme by rendering reference images on the streaming server and performing color restoration on the client. Extensive experiments on our prototype system demonstrate that CAGS outperforms the existing adaptive streaming systems in PSNR by 5$\sim$20 dB under fluctuating bandwidth, operates significantly faster than existing scalable Gaussian compression methods, and generalizes across different Gaussian representations.
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