ReorgGS: Equivalent Distribution Reorganization for 3D Gaussian Splatting

While a converged 3D Gaussian Splatting (3DGS) model may accurately approximate a target scene, its underlying parameterization often becomes severely ill-suited for further optimization. We identify this late-stage bottleneck as \emph{parameterization degeneration}: high-opacity floaters truncate gradient flow to background surfaces via alpha compositing, and redundant overlapping clusters cause severe parameter coupling with nearly collinear Jacobian responses. These structural barriers explain why continued optimization plateaus, even when removable artifacts persist. To break this deadlock, we propose ReorgGS, an equivalent distribution reorganization method. By treating the converged Gaussian set as an empirical probability field, ReorgGS resamples centers, estimates local anisotropic covariances via kNN, and initializes a low-opacity state before resuming optimization. Unlike standard opacity reset—which only rescales weights on a flawed topology—ReorgGS fundamentally rebuilds the spatial and visibility structure. Our analysis reveals a crucial insight: \emph{distributional equivalence does not imply optimization equivalence}. By preserving scene support while drastically improving gradient accessibility and reducing opacity-weighted overlap, ReorgGS provides a vastly superior optimization landscape. Under the same optimization budget and fixed Gaussian count, ReorgGS breaks the performance ceiling, suppresses persistent floaters, and reduces rendering overhead by eliminating redundant overlap.