S4GS: Resolution-Asymmetric 4K Feed-Forward Gaussian Reconstruction from Sparse Views

Feed-forward 3D Gaussian Splatting (3DGS) provides an efficient way to reconstruct renderable 3D scenes from sparse views, making it attractive for mobile immersive applications. However, immersive rendering demands 4K-level scene reconstruction, while directly processing such high-resolution inputs with large geometry backbones incurs prohibitive GPU memory costs. Existing high-resolution 3DGS methods mainly rely on per-scene optimization and are often enhanced by image or video super-resolution priors, making them difficult to support real-time immersive rendering. We present **S4GS**, a **resolution-asymmetric framework for 4K feed-forward Gaussian reconstruction from sparse views**. The key idea is to perform expensive scene-level reasoning at low resolution, while reserving high-resolution processing for dense geometry construction and local appearance refinement. For global reasoning, S4GS uses a low-resolution VGGT backbone to estimate camera parameters and build a coarse Gaussian scaffold. For high-resolution recovery, we anchor Gaussian positions using accurate dense depth maps, and employ a lightweight RGB branch to extract high-resolution image features and predict residual Gaussian attributes to restore fine details. By separating global 3D reasoning from high-resolution scene recovery, S4GS enables the full training and inference pipeline on a single 24 GB RTX 4090 GPU. Experiments on real-world mobile stereo data show that S4GS produces high-fidelity 4K novel views and supports variable-baseline stereo rendering for practical mobile-to-headset immersive applications.