StructSplat: Generalizable 3D Gaussian Splatting from Uncalibrated Sparse Views

We present StructSplat, a feed-forward and generalizable3D Gaussian reconstruction framework that operates directly on uncal-ibrated images without requiring camera parameters. Existing methodseither rely on per-scene optimization or assume known camera poses,and often entangle geometry and appearance within a unified backbone,limiting reconstruction fidelity and generalization. Our key idea is toadopt a structured representation that organizes geometry, seman-tic, and texture cues with explicit roles in the reconstruction process.Specifically, we introduce a pixel-aligned feature injection mechanismto enable accurate texture modeling from 2D observations, incorporatesemantic-aware priors to improve global consistency, and design a cameraalignment strategy to prevent information leakage and improve general-ization. Experiments show that our method significantly outperformsprior approaches on challenging benchmarks. On DL3DV, our methodachieves 28.045 PSNR, surpassing AnySplat (22.377) by +5.67 dB. Incross-dataset evaluation, our method achieves +1.94 dB over AnySplaton ACID and +1.72 dB on RealEstate10K.