JOIG: Joint Optimization Model of Image Features and Constraint Geometry Fusion for Generalizable Gaussian

Yang Shuo , Jianbo Zhang , Yongming Han , Liang Yuan ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.3233/faia251221

Novel View Synthesis (NVS) seeks to generate realistic novel views from limited source images, offering an effective solution for 3D reconstruction in complex or unknown environments. Achieving high generalization under occlusion, varying illumination, and sparse observations remains challenging, largely hinging on the effective extraction, optimization, and fusion of image features and spatial geometry. In this work, we propose JOIG — a Joint Optimization Model of Image Features and Constraint Geometry Fusion for generalizable 3D Gaussian splatting. JOIG introduces three key components: Multiscale Dimension Rotation Fusion (MDRF) to capture intrinsic dependencies across feature dimensions for enhanced image encoding, Geometry Self-Correcting Aggregation (GSCA) to refine multi-view geometry with depth-guided reweighting, and Geometry-Image Feature Aggregation (GIFA) to achieve pixel-aligned fusion of spatial and image information. Extensive experiments on DTU, LLFF, NeRF Synthetic, and Tanks and Temples datasets demonstrate that JOIG achieves state-of-the-art generalization performance, significantly improving both quantitative metrics and visual fidelity in novel view synthesis.