发表机构
Shanghai Jiao Tong University（上海交通大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出CRP-GS，一种利用跨表示先验的率失真优化压缩框架，通过对应导向层次结构和共享特征聚合改进锚点级熵建模，实现约30%平均比特率降低并保持渲染质量。
AI 中文摘要
3D高斯泼溅（3DGS）能够实现高质量的新视角合成，但由于密集的高斯图元而带来高昂的存储和传输成本。最近的基于锚点的压缩方法减少了每个图元的冗余，但锚点之间的冗余在很大程度上仍未得到利用。我们提出了CRP-GS（用于高斯泼溅的跨表示先验），这是一个率失真优化的压缩框架，利用跨表示先验来改进锚点级别的熵建模。首先，对应导向的层次结构（COHS）根据特征对应而非空间邻近性组织锚点，构建根-叶依赖关系，使得选定的锚点可以作为信息丰富的先验来条件编码其他锚点，从而产生更准确的似然预测和更低的条件熵。其次，共享特征聚合（SFA）从上下文哈希网格中提取全局共享特征，并将其注入锚点表示中，分解出场景一致的低频信息，否则这些信息会被冗余嵌入到各个锚点中。两个模块在统一的率失真目标下进行训练，以平衡比特率降低和渲染保真度。在多个基准上的实验表明，CRP-GS实现了有利的整体率失真权衡，与基于锚点的基线相比，平均比特率降低约30%，同时保持相当的渲染质量。
英文摘要
3D Gaussian Splatting (3DGS) enables high-quality novel view synthesis but incurs high storage and transmission costs due to dense Gaussian primitives. Recent anchor-based compression reduces per-primitive redundancy, yet redundancy across anchors remains largely unexploited. We propose CRP-GS (Cross-Representation Priors for Gaussian Splatting), a rate-distortion optimized compression framework that leverages cross-representation priors to improve anchor-level entropy modeling. First, a Correspondence-Oriented Hierarchical Structure (COHS) organizes anchors by feature correspondence rather than spatial proximity, constructing root-leaf dependencies so that selected anchors can act as informative priors to conditionally encode others, yielding more accurate likelihood prediction and lower conditional entropy. Second, Shared Feature Aggregation (SFA) extracts globally shared features from a contextual hash grid and injects them into anchor representations, factoring out scene-consistent low-frequency information that would otherwise be redundantly embedded in individual anchors. Both modules are trained under a unified rate-distortion objective to balance bitrate reduction and rendering fidelity. Experiments across multiple benchmarks show that CRP-GS achieves a favorable overall rate-distortion trade-off, yielding around 30% average bitrate reduction compared to anchor-based baselines while maintaining comparable rendering quality.
Comments14 pages, 8 figures. Accepted for publication in IEEE Transactions on Image Processing
