GeoSupSplat: Generalizable Sparse-View Surface Reconstruction via Geometry-Supervised Gaussian Splatting

Feed-forward Gaussian splatting enables fast reconstruction from sparse images, but photorealistic rendering could not yield an accurate surface. Geometry supervision in this setting faces two coupled challenges: pseudo-depth and other pretrained cues may be ambiguous or inconsistent across views, while the opacity used for RGB compositing does not always represent geometric visibility. We present $\text{GeoSupSplat}$, a feed-forward framework for reconstructing surfaces from two sparse images using 2D Gaussian surfels. Our key insight is that appearance visibility should not fully determine geometric confidence. GeoSupSplat therefore assigns each surfel separate appearance and geometry opacities, allowing RGB and depth to be composited independently while sharing the same center, scale, and orientation. Furthermore, we propose a set of novel geometry supervision techniques that enable the model to learn high-quality geometry. These include scale-invariant depth, depth-to-normal, and keypoint-based 3D consistency losses, which transfer relative depth structure, local surface orientation, and cross-view agreement without imposing the teacher's unknown scale. We evaluate our model on the Re10K dataset and two zero-shot cross-dataset benchmarks: ScanNet and Replica. Our model outperforms state-of-the-art methods in mesh quality and mesh-rendering tasks.