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KoalaGS: Complexity-Aware Continuous Level of Detail for Gaussian Splatting

Radu Beche, Raoul de Charette · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28615 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

コンピューティング予算の縮小下で訓練された3D Gaussian Splattingシーンを展開するには、より小さいプレフィックスでレンダリングするときに有効に残るための原始的なセットが必要です。この継続的なレベルの詳細(LoD)設定では、予算が減少するにつれて、どのGaussiansが容量を保持するべきかを決定することです。3DGS-MCMC再構築に関するパイロット研究では、最適化前に入力ビューから計算された画像の複雑性は、再構築エラーが画像と3Dスペースに集中している場所を予測します。それはまた、単純な地域があまりにも多くのGaussiansを含み、低予算で複雑なものと同じくらい劣化することを明らかにします。その後、KoalaGSでは、訓練

Machine translation (Meta M2M100); the English original follows.

Abstract

Deploying a trained 3D Gaussian Splatting scene under shrinking compute budgets requires one primitive set to remain effective when rendered at smaller prefixes. In this continuous level of detail (LoD) setting, the challenge is to decide which Gaussians should retain capacity as the budget decreases. A pilot study on 3DGS-MCMC reconstructions shows that image complexity, computed from the input views before optimization, predicts where reconstruction error concentrates in image and 3D space. It also reveals that simple regions contain too many Gaussians and degrade as much as complex ones at lower budgets, despite being inherently cheaper to represent. Subsequently, in KoalaGS, during training we use this complexity prior to bias relocation toward poorly reconstructed areas while ensuring smooth degradation across LoDs. A primitive importance score then enables inference at low-capacity, while preserving the scene structure. Tested across three datasets, KoalaGS maintains competitive full-capacity quality while significantly improving quality across the budget spectrum, yielding smoother transitions across LoDs, and enabling faster progressive streaming.

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Topics: Computer science, Artificial intelligence, Gaussian, Inference, Set (abstract data type), Computer vision

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