DP-Splat: Bayesian Nonparametric Complexity Control for Gaussian Splatting
3D Gaussian Splatting represents scenes as finite mixtures of anisotropic Gaussians whose number of components $K$ is set by heuristic density control or user caps. Variational Bayes Gaussian Splatting (VBGS) recast splat fitting as conjugate variational inference, but $K$ remains fixed. We replace the finite symmetric Dirichlet over mixture weights with a truncated stick-breaking Dirichlet-process prior (or a sparse overfitted finite Dirichlet), so that the number of occupied components adapts to the data while every update remains a closed-form coordinate-ascent step; a natural-gradient stochastic variant scales to large point sets. We give an ELBO monotonicity-and-convergence guarantee...
| Authors | Aqi Dong |
|---|---|
| Primary category | cs.CV |
| Published | 12 July 2026 |
| Last updated at source | 21 September 2026 |
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