We investigate what structure emerges in 3D Gaussian Splatting (3DGS) solutions from standard multi-view optimization. We term these Rendering-Optimal References (RORs) and analyze their statistical properties, revealing stable patterns: mixture-structured scales and bimodal radiance across diverse scenes. To understand what determines these parameters, we apply learnability probes by training predictors to reconstruct RORs from point clouds without rendering supervision. Our analysis uncovers fundamental density-stratification. Dense regions exhibit geometry-correlated parameters amenable to render-free prediction, while sparse regions show systematic failure across architectures. We formalize this through variance decomposition, demonstrating that visibility heterogeneity creates covariance-dominated coupling between geometric and appearance parameters in sparse regions. This reveals the dual character of RORs: geometric primitives where point clouds suffice, and view synthesis primitives where multi-view constraints are essential. We provide density-aware strategies that improve training robustness and discuss architectural implications for systems that adaptively balance feed-forward prediction and rendering-based refinement.

翻译：本研究探究标准多视角优化下3D高斯溅射（3DGS）解中涌现的结构特征。我们将此类解定义为渲染最优参考（RORs），并通过统计分析揭示其稳定模式：在不同场景中呈现混合结构的尺度分布与双模态辐射特性。为探究参数决定机制，我们采用可学习性探针方法，训练预测器在无需渲染监督的条件下从点云重建RORs。分析发现根本性的密度分层现象：密集区域表现出与几何相关的参数特征，可通过免渲染预测有效重建；而稀疏区域则在不同架构中均呈现系统性失效。通过方差分解形式化证明，可见性异质性导致稀疏区域几何参数与外观参数产生协方差主导的耦合效应。这揭示了RORs的双重本质：在点云充足的区域表现为几何基元，而在需要多视角约束的区域则成为视图合成基元。我们提出密度感知策略以提升训练鲁棒性，并探讨自适应平衡前馈预测与基于渲染优化的系统架构设计启示。