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.

翻译：从单一模型中实现可控保真度的渲染能力，即层级细节（LoD），对于三维高斯飞溅（3DGS）的实际部署至关重要。现有离散LoD方法仅提供有限的操作点，而连续LoD方法虽能实现平滑缩放，但在全容量状态下常伴随显著的渲染质量下降，使得LoD成为成本高昂的设计抉择。我们提出马特廖什卡高斯飞溅（MGS），这是一种训练框架，能在不牺牲全容量渲染质量的前提下，为标准3DGS流程实现连续LoD。MGS学习单一有序高斯集，使得对任一前缀（即前k个飞溅点）进行渲染时，能产生随预算增加而平滑改善保真度的连贯重建结果。其核心思想是随机预算训练：每次迭代采样随机飞溅预算，同时优化对应前缀与全集。该策略仅需两次前向传播，且无需修改架构。在四个基准测试与六个基线方法的实验表明，MGS在匹配其主干模型全容量性能的同时，可从单一模型实现连续的速度-质量权衡。基于排序策略、训练目标与模型容量的广泛消融实验进一步验证了设计有效性。