Matryoshka Gaussian Splatting
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866910061169737728 |
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| author | Guo, Zhilin Zhang, Boqiao Aktas, Hakan Fogarty, Kyle Hu, Jeffrey Aslan, Nursena Koprucu Li, Wenzhao Baykal, Canberk Miao, Albert Bengtson, Josef Zhou, Chenliang Xia, Weihao Vasconcelos, Cristina Nader Oztireli, Cengiz |
| author_facet | Guo, Zhilin Zhang, Boqiao Aktas, Hakan Fogarty, Kyle Hu, Jeffrey Aslan, Nursena Koprucu Li, Wenzhao Baykal, Canberk Miao, Albert Bengtson, Josef Zhou, Chenliang Xia, Weihao Vasconcelos, Cristina Nader Oztireli, Cengiz |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_19234 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Matryoshka Gaussian Splatting Guo, Zhilin Zhang, Boqiao Aktas, Hakan Fogarty, Kyle Hu, Jeffrey Aslan, Nursena Koprucu Li, Wenzhao Baykal, Canberk Miao, Albert Bengtson, Josef Zhou, Chenliang Xia, Weihao Vasconcelos, Cristina Nader Oztireli, Cengiz Computer Vision and Pattern Recognition Graphics 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. |
| title | Matryoshka Gaussian Splatting |
| topic | Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/2603.19234 |