让3D高斯模型可按需压缩，一模型支持多清晰度，适配不同设备
GoDe: Gaussians on Demand for Progressive Level of Detail and Scalable Compression
- 用梯度敏感度排序高斯点，构建从粗到细的渐进层级
- 单模型实现多码率压缩，性能媲美专门优化的单码率方法
- 无需重训练，适合移动端或动态带宽场景部署
近期在显式辐射场表示（尤其是3D Gaussian Splatting）的压缩方面取得进展，显著降低内存占用并提升实时渲染性能。然而，现有方法本质上为单码率：每个压缩级别需单独优化模型，导致固定的工作点，难以适应内存、带宽或计算预算随设备或时间变化的场景。我们主张可扩展性应是表示的固有属性。我们发现，训练好的显式辐射模型具有结构化的信息分布，可通过训练中常见的优化信号揭示。特别是，聚合梯度敏感度提供了一个简单且模型无关的标准，用于将原始体素从粗略结构组织到精细细节。基于此，我们提出GoDe（Gaussians on Demand），一种针对3D Gaussian Splatting的通用可扩展压缩与渐进细节控制框架。从单一训练模型出发，GoDe将高斯原始体素重新组织为固定渐进层级，支持多码率-失真工作点，无需重训练或逐级微调。一次量化感知微调确保所有层级在低精度存储下行为一致。在标准基准和多个3D Gaussian Splatting骨干网络上的大量实验表明，GoDe在码率-失真性能上可媲美最先进的单码率方法，同时实现了真正的可扩展压缩与统一表示内的自适应渲染。
原文摘要 · Abstract (English)
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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