将3D高斯点云渲染放到云端，手机端低延迟交互体验。
Thin-Client Interactive Gaussian Adaptive Streaming over HTTP/3
- 云端渲染+自适应流传输，降低客户端算力需求
- 网络波动下仍保持60帧内响应，平均SSIM达0.88
- 适合移动端与XR设备的实时3D内容展示
3D高斯点云（3DGS）技术实现了复杂场景的逼真渲染，但其在移动和扩展现实（XR）设备上的普及受限于巨大的计算与带宽需求。现有方案多聚焦于客户端模型压缩，但仍需大量GPU算力，难以在资源受限设备上运行。本文提出TIGAS（Thin-client Interactive Gaussian Adaptive Streaming），一种将光栅化过程移至后端的远程渲染框架。为克服网络波动带来的高延迟问题，TIGAS通过QUIC协议向轻量级网页客户端流式传输视图相关的2D投影，有效减少队头阻塞。配套的自适应码率算法根据网络状况动态调整渲染质量，在6自由度（6DoF）交互约束下维持极低运动到像素延迟。此外，我们引入实验性WebGPU超分辨率管线，分析感知质量提升与客户端处理瓶颈之间的权衡。我们在跨大陆环境中对14个3DGS模型及真实的6DoF EyeNavGS运动轨迹进行了全面评估。后端渲染帧时间低于10毫秒，系统始终满足交互延迟阈值，平均结构相似性（SSIM）达0.88，既可作为3DGS流媒体研究的可靠测试平台，也可作为实际部署的交付系统。源代码已开源：https://github.com/Rekenar/GaussianAdaptiveStreamer。
原文摘要 · Abstract (English)
Recent advancements in 3D Gaussian Splatting (3DGS) have enabled photorealistic rendering of complex scenes, yet widespread adoption on mobile and Extended Reality (XR) devices is hindered by substantial computational and bandwidth requirements. While existing solutions often focus on model compression for client-side rendering, they still demand significant GPU power, limiting applicability on resource-constrained hardware. We propose TIGAS (Thin-client Interactive Gaussian Adaptive Streaming), a remote rendering framework offloading rasterization to a backend. To bypass the prohibitive latencies connected to fluctuating network conditions, TIGAS streams view-dependent 2D projections to a lightweight web client over QUIC, minimizing head-of-line (HoL) blocking. A dedicated ABR algorithm adapts rendering quality to fluctuating network conditions, maintaining motion-to-photon latency within strict 6DoF interactive constraints. Furthermore, we discuss the integration of an experimental WebGPU super-resolution pipeline to analyze the trade-offs between perceptual quality enhancements and thin-client processing bottlenecks. We extensively evaluate TIGAS across multi-continental environments using 14 3DGS models and real 6DoF EyeNavGS movement traces. Powered by a backend rendering frames in under 10 milliseconds, TIGAS maintains latency within interactive thresholds while achieving an average SSIM of 0.88, serving both as a robust testbed for 3DGS streaming research and a capable delivery system. The source code is available at: https://github.com/Rekenar/GaussianAdaptiveStreamer.
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