CLM: Removing the GPU Memory Barrier for 3D Gaussian Splatting

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 substantial memory requirement, which exceeds the memory capacity of most GPUs. 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 improve performance and reduce communication overheads, CLM uses a novel offloading strategy based on insights into 3DGS's memory access patterns. This strategy enables efficient pipelining, which overlaps GPU-to-CPU communication, GPU computation and CPU computation. Furthermore, CLM exploits these access patterns to reduce communication volume. Our evaluation shows that the resulting implementation can render a large scene that requires 102 million Gaussians on a single RTX4090 and achieve state-of-the-art reconstruction quality. The code is open-sourced at: https://github.com/nyu-systems/CLM-GS