Analysis of Converged 3D Gaussian Splatting Solutions: Density Effects and Prediction Limit
This work addresses the problem of understanding and optimizing 3D Gaussian Splatting for computer vision and graphics researchers, offering incremental insights into parameter prediction and training robustness.
The paper analyzes the structure of 3D Gaussian Splatting solutions, revealing stable patterns like mixture-structured scales and bimodal radiance, and identifies density-stratification where dense regions allow render-free prediction while sparse regions fail, leading to density-aware strategies for improved training robustness.
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.