Rendering-Optimal Is Not Mesh-Optimal: Composing Multi-Budget Gaussians for Compact Surface Extraction

3D Gaussian Splatting (3DGS) achieves high-quality rendering, but its Gaussians do not form an explicit mesh. Gaussian-based surface reconstruction methods fit a surface to the learned Gaussians and mesh it on a spatial structure built around them, yielding an explicit mesh. In these methods, mesh complexity is largely determined by the spatial distribution of Gaussians, with denser Gaussians tending to yield denser triangulation. Obtaining a compact yet detailed mesh therefore depends on allocating Gaussians densely where the geometry is complex and sparsely elsewhere. However, the placement and density of these Gaussians are driven by photometric supervision and may not align with the geometry required for compact mesh reconstruction. We address this mismatch by composing Gaussians trained at different budgets, i.e. Gaussian counts, into a single set of Gaussians, drawing each region from the budget its geometry needs. Starting from the lowest budget, we keep its Gaussians where they already capture the geometry and replace them with higher-budget Gaussians only where rendered depth indicates a sufficient geometric gain. The resulting composite is then meshed with the underlying reconstruction method's own extraction procedure, yielding more compact meshes while preserving geometric quality. Across multiple Gaussian-based surface reconstruction methods and datasets, our composites improve the geometry–mesh-size trade-off, reducing mesh size by 26–59% relative to extraction from the finest budget while maintaining comparable or better geometric accuracy and rendering quality.