3D Gaussian Splatting enhances real-time performance in novel view synthesis by representing scenes with mixtures of Gaussians and utilizing differentiable rasterization. However, it typically requires large storage capacity and high VRAM, demanding the design of effective pruning and compression techniques. Existing methods, while effective in some scenarios, struggle with scalability and fail to adapt models based on critical factors such as computing capabilities or bandwidth, requiring to re-train the model under different configurations. In this work, we propose a novel, model-agnostic technique that organizes Gaussians into several hierarchical layers, enabling progressive Level of Detail (LoD) strategy. This method, combined with recent approach of compression of 3DGS, allows a single model to instantly scale across several compression ratios, with minimal to none impact to quality compared to a single non-scalable model and without requiring re-training. We validate our approach on typical datasets and benchmarks, showcasing low distortion and substantial gains in terms of scalability and adaptability.
3D高斯点云（3DGS）通过使用高斯混合表示场景并利用可微光栅化技术，在新视角合成中提升了实时性能。然而，它通常需要较大的存储容量和高显存，要求设计有效的剪枝和压缩技术。现有的方法虽然在某些场景下有效，但在可扩展性方面存在困难，且无法根据计算能力或带宽等关键因素调整模型，通常需要在不同配置下重新训练模型。在本工作中，我们提出了一种新颖的、与模型无关的技术，将高斯点云组织为多个层级，从而实现渐进的细节层次（Level of Detail, LoD）策略。该方法结合了最近的3DGS压缩方法，使得单个模型能够在多个压缩比下即时扩展，且与单个不可扩展模型相比，几乎不影响质量，且无需重新训练。我们在典型数据集和基准测试中验证了该方法，展示了低失真和显著的可扩展性与适应性提升。