BluePrint-GS: Persistent Representation Demand for Adaptive 3D Gaussian Growth

3D Gaussian Splatting (3DGS) reconstructs photorealistic scenes that can be rendered from arbitrary viewpoints. It attains this fidelity by progressively densi- fying Gaussian primitives wherever reconstruction remains imperfect. However, such densification commonly produces millions of primitives for complex scenes, straining memory, storage, and rendering. This redundancy persists because the signals that trigger growth are ambiguous: a large error may indicate missing representation or merely unconverged Gaussians, and a genuinely unrepresented region may elicit no response at all. Densification therefore adds Gaussians where none are needed and compounds the redundancy by splitting them further. To suppress the redundancy, we present BluePrint-GS, a demand-driven framework that decides where to place new Gaussian primitives from the state of the scene rather than the state of individual primitives. BluePrint-GS maintains representation demand, a persistent world-space state that records where representation remains insufficient. Particularly, BluePrint-GS determines where additional representation is needed, constructs new Gaussian geometry from the spatial structure of local demand, and quantifies how much to introduce through conserved representation intensity, so that growth follows demand rather than a prescribed target population, and redundancy is suppressed. Experimental results demonstrate that, across 13 scenes from Mip-NeRF 360, Deep Blending, and Tanks and Temples, BluePrint-GS reduces the Gaussian population by 86.0%, 90.0%, and 87.7%, respectively, relative to vanilla 3DGS, with average PSNR losses of at most 0.45 dB. These results demonstrate a competitive quality-compactness trade-off for demand-guided Gaussian growth.