GS-Cache让大模型3D高斯渲染在消费级设备上实现120帧以上实时运行
GS-Cache: A GS-Cache Inference Framework for Large-scale Gaussian Splatting Models
- 构建缓存驱动的渲染流水线，消除重复计算
- 支持2K双目120帧以上渲染，延迟降低35%，显存减少42%
- 适合虚拟现实、实时神经渲染场景的开发者使用
大规模3D高斯点云渲染在消费级设备上实现实时高保真表现面临巨大挑战。为在虚拟现实（VR）等应用中充分发挥3DGS潜力，需解决关键系统级难题以支持实时沉浸体验。本文提出GS-Cache，一种端到端框架，将3DGS的先进表示与高度优化的渲染系统无缝集成。该框架引入以缓存为中心的流水线，消除冗余计算；设计效率感知调度器，支持弹性多GPU渲染；并优化CUDA内核以突破计算瓶颈。3DGS与系统设计的协同使GS-Cache实现最高5.35倍性能提升，35%延迟降低，42%显存占用减少，支持2K双目渲染超过120 FPS且保持高视觉质量。通过弥合3DGS表示能力与VR系统需求之间的差距，GS-Cache为沉浸式环境中的实时神经渲染建立了可扩展、高效的新范式。
原文摘要 · Abstract (English)
Rendering large-scale 3D Gaussian Splatting (3DGS) model faces significant challenges in achieving real-time, high-fidelity performance on consumer-grade devices. Fully realizing the potential of 3DGS in applications such as virtual reality (VR) requires addressing critical system-level challenges to support real-time, immersive experiences. We propose GS-Cache, an end-to-end framework that seamlessly integrates 3DGS's advanced representation with a highly optimized rendering system. GS-Cache introduces a cache-centric pipeline to eliminate redundant computations, an efficiency-aware scheduler for elastic multi-GPU rendering, and optimized CUDA kernels to overcome computational bottlenecks. This synergy between 3DGS and system design enables GS-Cache to achieve up to 5.35x performance improvement, 35% latency reduction, and 42% lower GPU memory usage, supporting 2K binocular rendering at over 120 FPS with high visual quality. By bridging the gap between 3DGS's representation power and the demands of VR systems, GS-Cache establishes a scalable and efficient framework for real-time neural rendering in immersive environments.
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