GeoSplat: Generalizable 3D Gaussian Splatting from Sparse Multi-View Images with Geometry-Aware Priors

Generalizable 3D Gaussian Splatting enables efficient sparse-view novel view synthesis, but accurate Gaussian center estimation remains challenging. Epipolar attention and cost volume methods exhibit complementary limitations in non-Lambertian regions, appearance-ambiguous areas, and scenes with large perspective changes. These limitations lead to feature mismatches, depth estimation errors, and geometric distortions. To address these limitations, we propose GeoSplat, a feed-forward Generalizable 3D Gaussian Splatting framework with geometry-aware priors. Specifically, we introduce a geometry-aware cost volume that injects relative pose distance, view-dependent ray angle, and spatial validity masks into dense depth matching, enabling the network to jointly reason about photometric consistency, triangulation reliability, and visibility. Furthermore, we design a ray-guided iterative refinement module, in which full-resolution 3D ray direction and depth confidence priors jointly guide recurrent residual updates to progressively refine coarse depth predictions in a continuous space. Extensive experiments on RealEstate10K and ACID demonstrate that GeoSplat achieves competitive reconstruction quality with a compact parameter count, while presenting an accuracy efficiency trade-off.