Matryoshka Gaussian Splatting
The ability to render scenes at adjustable fidelity from a single model, known as level of detail (LoD), is crucial for practical deployment of 3D Gaussian Splatting (3DGS). Existing discrete LoD methods expose only a limited set of operating points, while concurrent continuous LoD approaches enable smoother scaling but often suffer noticeable quality degradation at full capacity, making LoD a costly design decision. We introduce Matryoshka Gaussian Splatting (MGS), a training framework that enables continuous LoD for standard 3DGS pipelines without sacrificing full-capacity rendering quality. MGS learns a single ordered set of Gaussians such that rendering any prefix, the first k splats, produces a coherent reconstruction whose fidelity improves smoothly with increasing budget. Our key idea is stochastic budget training: each iteration samples a random splat budget and optimises both the corresponding prefix and the full set. This strategy requires only two forward passes and introduces no architectural modifications. Experiments across four benchmarks and six baselines show that MGS matches the full-capacity performance of its backbone while enabling a continuous speed-quality trade-off from a single model. Extensive ablations on ordering strategies, training objectives, and model capacity further validate the designs.
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Zhilin Guo
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Boqiao Zhang
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Hakan Aktas
69bcae9e081b9ad49af571fc
Kyle Fogarty
69bcae9e081b9ad49af571fd
Jeffrey Hu
69bcae9e081b9ad49af571fe
Nursena Koprucu Aslan
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Wenzhao Li
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Canberk Baykal
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Albert Miao
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Josef Bengtson
69bcae9e081b9ad49af57203
Chenliang Zhou
69bcae9e081b9ad49af57204
Weihao Xia
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Cristina Nader Vasconcelos. Cengiz Oztireli
Matryoshka Gaussian Splatting
The ability to render scenes at adjustable fidelity from a single model, known as level of detail (LoD), is crucial for practical deployment of 3D Gaussian Splatting (3DGS). Existing discrete LoD methods expose only a limited set of operating points, while concurrent continuous LoD approaches enable smoother scaling but often suffer noticeable quality degradation at full capacity, making LoD a costly design decision. We introduce Matryoshka Gaussian Splatting (MGS), a training framework that enables continuous LoD for standard 3DGS pipelines without sacrificing full-capacity rendering quality. MGS learns a single ordered set of Gaussians such that rendering any prefix, the first k splats, produces a coherent reconstruction whose fidelity improves smoothly with increasing budget. Our key idea is stochastic budget training: each iteration samples a random splat budget and optimises both the corresponding prefix and the full set. This strategy requires only two forward passes and introduces no architectural modifications. Experiments across four benchmarks and six baselines show that MGS matches the full-capacity performance of its backbone while enabling a continuous speed-quality trade-off from a single model. Extensive ablations on ordering strategies, training objectives, and model capacity further validate the designs.
Matryoshka Gaussian Splatting enables continuous level of detail rendering by training a single ordered set of Gaussians that maintains full-capacity quality while allowing smooth quality-scaling trade-offs.
3D Gaussian Splatting level of detail continuous LoD stochastic budget training Gaussian splatting rendering quality speed-quality trade-off