TideGS: Scalable Training of Over One Billion 3D Gaussian Splatting Primitives via Out-of-Core Optimization

Training 3D Gaussian Splatting (3DGS) at billion-primitive scale is fundamentally memory-bound: each Gaussian primitive carries a large attribute vector, and the aggregate parameter table quickly exceeds GPU capacity, limiting prior systems to tens of millions of Gaussians on commodity single-GPU hardware. We observe that 3DGS training is inherently sparse and trajectory-conditioned: each iteration activates only the Gaussians visible from the current camera batch, so GPU memory can serve as a working-set cache rather than a persistent parameter store. Building on this insight, we introduce TideGS, an out-of-core training framework that manages parameters across an SSD-CPU-GPU hierarchy via three synergistic techniques: block-virtualized geometry for SSD-aligned spatial locality, a hierarchical asynchronous pipeline to overlap I/O with computation, and trajectory-adaptive differential streaming that transfers only incremental working-set deltas between iterations. Experiments show that TideGS enables training with over one billion Gaussians on a single 24-GB GPU while achieving the best reconstruction quality among evaluated single-GPU baselines on large-scale scenes, scaling beyond prior out-of-core baselines (e.g., ~100M Gaussians) and standard in-memory training (e.g., ~11M Gaussians). Project page: https://sponge-lab.github.io/TideGS/

3D Gaussian Splatting is widely used to reconstruct and render 3D scenes, but training very large scenes can require more GPU memory than is available on a single consumer GPU. This memory limitation makes large-scale reconstruction difficult without expensive hardware. TideGS addresses this problem by storing most of the scene data outside the GPU and bringing only the currently needed parts into GPU memory during training. It also organizes the scene data to make loading more efficient and to reuse data across nearby camera views. With these techniques, TideGS can train scenes with over one billion Gaussians on a single 24 GB GPU while maintaining high reconstruction quality.