Dual-Splatting for Efficient UHD 3D Gaussian Splatting Super-Resolution

3D Gaussian Splatting (3DGS) enables novel view synthesis with high visual quality, but its rendering quality remains limited by the resolution of captured observations. To support close inspection, recent 3DGS super-resolution methods introduce high-frequency priors from pretrained single image super-resolution (SISR) models. However, fitting generated RGB details can require dense Gaussian primitives and high resolution rasterization, limiting ultra high definition (UHD) scalability on consumer hardware. To address this issue, we propose Dual-Splatting, a framework that integrates the SISR latent generative process into a dual branch representation. The RGB branch reconstructs scene structure and appearance from low resolution observations, while the latent branch models high-frequency details through SISR latent priors. The two branches share geometry to align scene reconstruction and detail synthesis. A latent refinement head transforms features rendered at the input resolution into latents for the frozen SISR decoder, enabling detail synthesis without Gaussian rasterization at the target resolution. To further improve consistency across views, we introduce multiview regularization in latent space that aggregates reprojected predictions from neighboring views. Experiments across multiple datasets and resolutions show compact scene representations and improved consistency over independent SISR, with practical 4K/6K super-resolution on a 24 GB GPU.