让3D高斯点渲染突破显存限制，单张消费级显卡也能处理上亿点云。
CLM: Removing the GPU Memory Barrier for 3D Gaussian Splatting
- 将高斯点暂存于CPU内存，按需加载到GPU，突破显存瓶颈。
- 通过优化数据调度策略，实现计算与通信重叠，提升运行效率。
- 实测可在单张RTX4090上渲染需1亿高斯点的大场景，效果领先。
3D高斯点渲染（3DGS）因其快速渲染和高质量输出而日益流行。然而，其巨大的内存需求使其难以扩展至大型或复杂场景，超出多数GPU的显存容量。本文提出CLM系统，使3DGS能在单张消费级显卡（如RTX4090）上渲染大场景。该系统将高斯点暂存于CPU内存，仅在需要时加载至GPU。为降低性能与通信开销，CLM利用3DGS的内存访问模式，设计新型卸载策略以实现流水线化，从而重叠GPU与CPU间的通信、计算过程。此外，还通过分析访问模式减少通信量。评估表明，该系统可在单张RTX4090上渲染需1亿高斯点的大场景，并达到当前最优重建质量。
原文摘要 · Abstract (English)
3D Gaussian Splatting (3DGS) is an increasingly popular novel view synthesis approach due to its fast rendering time, and high-quality output. However, scaling 3DGS to large (or intricate) scenes is challenging due to its large memory requirement, which exceed most GPU's memory capacity. In this paper, we describe CLM, a system that allows 3DGS to render large scenes using a single consumer-grade GPU, e.g., RTX4090. It does so by offloading Gaussians to CPU memory, and loading them into GPU memory only when necessary. To reduce performance and communication overheads, CLM uses a novel offloading strategy that exploits observations about 3DGS's memory access pattern for pipelining, and thus overlap GPU-to-CPU communication, GPU computation and CPU computation. Furthermore, we also exploit observation about the access pattern to reduce communication volume. Our evaluation shows that the resulting implementation can render a large scene that requires 100 million Gaussians on a single RTX4090 and achieve state-of-the-art reconstruction quality.
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