SwinGS：用于任意长度体积视频流式传输的滑动窗口高斯泼溅
SwinGS: Sliding Window Gaussian Splatting for Volumetric Video Streaming with Arbitrary Length
AI总结：
SwinGS提出滑动窗口高斯泼溅框架，结合时空高斯与MCMC，实现任意长度体积视频的实时流式传输，传输成本降低83.6%。
摘要：
三维高斯泼溅（3DGS）的最新进展因其高渲染速度和卓越质量而在计算机视觉和计算机图形学领域引起了广泛关注。尽管现有研究已尝试将3DGS的应用从静态场景扩展到动态场景，但此类努力一直受到模型尺寸过大、视频时长限制和内容偏差的阻碍。这些限制严重损害了动态三维高斯模型的可流式传输性，从而限制了它们在体积视频、自动驾驶车辆以及虚拟现实、增强现实和混合现实等沉浸式技术等下游应用中的实用性。本文介绍了SwinGS，一个用于以实时流式传输方式训练、交付和渲染体积视频的新型框架。为了解决上述挑战并增强可流式传输性，SwinGS将时空高斯与马尔可夫链蒙特卡洛（MCMC）方法相结合，使模型能够适应跨帧的各种三维场景，同时采用滑动窗口以累积方式捕获每帧的高斯快照。我们实现了SwinGS的原型，并展示了其在各种数据集和场景中的可流式传输性。此外，我们开发了一个交互式WebGL查看器，支持在大多数具有现代浏览器的设备（包括智能手机和平板电脑）上进行实时体积视频播放。实验结果表明，与先前工作相比，SwinGS将传输成本降低了83.6%，并且可以轻松扩展到任意长度的体积视频，而无需增加所需的GPU资源。
英文摘要：
Recent advances in 3D Gaussian Splatting (3DGS) have garnered significant attention in computer vision and computer graphics due to its high rendering speed and remarkable quality. While extant research has endeavored to extend the application of 3DGS from static to dynamic scenes, such efforts have been consistently impeded by excessive model sizes, constraints on video duration, and content deviation. These limitations significantly compromise the streamability of dynamic 3D Gaussian models, thereby restricting their utility in downstream applications, including volumetric video, autonomous vehicle, and immersive technologies such as virtual, augmented, and mixed reality. This paper introduces SwinGS, a novel framework for training, delivering, and rendering volumetric video in a real-time streaming fashion. To address the aforementioned challenges and enhance streamability, SwinGS integrates spacetime Gaussian with Markov Chain Monte Carlo (MCMC) to adapt the model to fit various 3D scenes across frames, in the meantime employing a sliding window captures Gaussian snapshots for each frame in an accumulative way. We implement a prototype of SwinGS and demonstrate its streamability across various datasets and scenes. Additionally, we develop an interactive WebGL viewer enabling real-time volumetric video playback on most devices with modern browsers, including smartphones and tablets. Experimental results show that SwinGS reduces transmission costs by 83.6% compared to previous work and could be easily scaled to volumetric videos with arbitrary length with no increasing of required GPU resources.
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