A LoD of Gaussians: Unified Training and Rendering for Ultra-Large Scale Reconstruction with External Memory

TL;DR

This paper presents A LoD of Gaussians, a novel framework that enables real-time, large-scale scene reconstruction and rendering on a single GPU by out-of-core storage and dynamic streaming of Gaussian representations.

Contribution

It introduces a unified, out-of-core training and rendering method for ultra-large scenes without partitioning, using a hybrid data structure and LoD representation.

Findings

Supports seamless multi-scale scene visualization

Enables real-time rendering of city-scale environments

Operates efficiently on consumer-grade GPUs

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 support real-time streaming and rendering. Together, these innovations enable seamless multi-scale reconstruction and interactive visualization of complex scenes -- from broad aerial views to fine-grained ground-level details.