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

• URL: http://arxiv.org/abs/2507.01110v2
• Date: Sat, 05 Jul 2025 15:51:57 GMT
• Title: A LoD of Gaussians: Unified Training and Rendering for Ultra-Large Scale Reconstruction with External Memory
• Authors: Felix Windisch, Lukas Radl, Thomas Köhler, Michael Steiner, Dieter Schmalstieg, Markus Steinberger,
• Abstract summary: We introduce A LoD of Gaussians, a framework for training and rendering ultra-large-scale scenes on a single consumer-grade GPU.<n>A hybrid data structure combining Gaussian hierarchies with Sequential Point Trees enables efficient, view-dependent LoD selection.<n>A lightweight caching and view scheduling system exploits temporal coherence to support real-time streaming and rendering.
• Score: 8.972911362220803
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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