GoDe：为渐进细节层次和可扩展压缩的高斯
GoDe: Gaussians on Demand for Progressive Level of Detail and Scalable Compression
AI总结：
GoDe通过重新组织高斯原始体，实现可扩展压缩和渐进细节控制，无需重新训练或微调，提升内存和带宽效率。
摘要：
最近在压缩显式辐射场表示方面取得的进步，特别是3D高斯点云，显著降低了内存消耗并提高了实时渲染性能。然而，现有方法本质上是单速率的：每个压缩级别需要单独优化的模型，产生固定操作点而非真正的可扩展表示。我们提出GoDe框架，通过训练的显式辐射模型的信息分布结构，利用训练中的标准优化信号，将原始体从粗结构到细改进进行组织。GoDe为3D高斯点云提供了一种通用框架，从单个训练模型出发，重新组织高斯原始体为固定渐进层次，支持多种率-失真操作点，无需重新训练或逐层微调。单次量化感知微调确保在低精度存储下行为一致。在标准基准和多个3D高斯点云基础上的实验表明，GoDe在率-失真性能上与最先进的单速率方法相当，同时实现真正的可扩展压缩和适应性渲染。
英文摘要：
Recent progress in compressing explicit radiance field representations, particularly 3D Gaussian Splatting, has substantially reduced memory consumption while improving real-time rendering performance. However, existing approaches remain inherently single-rate: each compression level requires a separately optimized model, yielding a set of fixed operating points rather than a truly scalable representation. This limits deployment in scenarios where memory, bandwidth, or computational budgets vary across devices or over time. We argue that scalability should be an intrinsic property of the representation. We show that trained explicit radiance models exhibit a structured distribution of information, which can be revealed using standard optimization signals available during training. In particular, aggregated gradient sensitivity provides a simple, model-agnostic criterion to organize primitives from coarse structure to finer refinements. Building on this, we introduce GoDe (Gaussians on Demand), a general framework for scalable compression and progressive level-of-detail control, instantiated for 3D Gaussian Splatting. Starting from a single trained model, GoDe reorganizes Gaussian primitives into a fixed progressive hierarchy supporting multiple rate-distortion operating points without retraining or per-level fine-tuning. A single quantization-aware fine-tuning stage ensures consistent behavior across all levels under low-precision storage. Extensive experiments on standard benchmarks and multiple 3D Gaussian Splatting backbones show that GoDe achieves rate-distortion performance comparable to state-of-the-art single-rate methods, while enabling truly scalable compression and adaptive rendering within a unified representation. Project page: https://gaussians-on-demand.github.io
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