Title: A LoD of Gaussians: Unified Training and Rendering for Ultra-Large Scale Reconstruction with External Memory
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Gaussian Splatting, Neural Rendering, Level of Detail, city-scale, Sequential Point Trees

Abstract: Gaussian Splatting has emerged as a high-performance technique for novel view synthesis, enabling real-time rendering and high-quality reconstruction of small scenes. However, scaling to larger environments has so far relied on partitioning the scene into chunks---a strategy that introduces artifacts at chunk boundaries, complicates training across varying scales, and is poorly suited to unstructured scenarios such as city-scale flyovers combined with street-level views. Moreover, rendering remains fundamentally limited by GPU memory, as all visible chunks must reside in VRAM simultaneously.
We introduce A LoD of Gaussians, a framework for training and rendering ultra-large-scale Gaussian scenes on a single consumer-grade GPU---without partitioning. Our method stores the full scene out-of-core (e.g., in CPU memory) and trains a Level-of-Detail (LoD) representation directly, dynamically streaming only the relevant Gaussians. A hybrid data structure combining Gaussian hierarchies with Sequential Point Trees enables efficient, view-dependent LoD selection, while a lightweight caching and view scheduling system exploits temporal coherence to minimize the loading overhead. Together, these innovations enable seamless multi-scale reconstruction and interactive visualization of complex scenes---from broad aerial views to fine-grained ground-level details.

TL;DR: Out-of-core memory can be used to expand the scale of 3D Gaussian Splatting training without requiring scene partition.