LODGE: Level-of-Detail Large-Scale Gaussian Splatting with Efficient Rendering

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00387802" target="_blank" >RIV/68407700:21230/25:00387802 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21730/25:00387802

Result on the web

<a href="https://openreview.net/pdf?id=Iqu63cYI3z" target="_blank" >https://openreview.net/pdf?id=Iqu63cYI3z</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

LODGE: Level-of-Detail Large-Scale Gaussian Splatting with Efficient Rendering

Original language description

In this work, we present a novel level-of-detail (LOD) method for 3D Gaus sian Splatting that enables real-time rendering of large-scale scenes on memory constrained devices. Our approach introduces a hierarchical LOD representation that iteratively selects optimal subsets of Gaussians based on camera distance, thus largely reducing both rendering time and GPU memory usage. We construct each LODlevel by applying a depth-aware 3D smoothing filter, followed by importance based pruning and fine-tuning to maintain visual fidelity. To further reduce memory overhead, we partition the scene into spatial chunks and dynamically load only relevant Gaussians during rendering, employing an opacity-blending mechanism to avoid visual artifacts at chunk boundaries. Our method achieves state-of-the-art performance on both outdoor (Hierarchical 3DGS) and indoor (Zip-NeRF) datasets, delivering high-quality renderings with reduced latency and memory requirements.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Advances in Neural Information Processing Systems 38 (NeurIPS 2025)

ISBN

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ISSN

1049-5258

e-ISSN

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Number of pages

22

Pages from-to

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Publisher name

Neural Information Processing Systems Foundation, Inc.

Place of publication

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Event location

San Diego

Event date

Dec 2, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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