GeoSplat: Generalizable 3D Gaussian Splatting from Sparse Multi-View Images with Geometry-Aware Priors
TL;DR
This work introduces 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.
Abstract
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.