CoSplat: Complementary Surface Geometry Learning for Generalizable Surface Reconstruction

Feed-forward Gaussian Splatting enables efficient surface reconstruction from sparse views, yet the geometry of its predicted primitives remains ambiguous under rendering-driven supervision. Specifically, distinct primitive configurations can produce similar renderings, as geometric errors can be compensated for by adjustments to appearance parameters. To resolve this ambiguity, we propose CoSplat, a feed-forward 2DGS framework that explicitly constrains surfel geometry. We first introduce Geometry-Calibrated Normal Supervision to directly regularize primitive orientations and mitigate geometric drift caused by geometry-appearance compensation. Since local normal constraints are insufficient to recover globally coherent geometry from sparse views, we further propose Confidence-Guided Tangent-Plane Depth Propagation, which translates reliable local orientations into surface-aligned depth hypotheses and validates them using multi-view geometric evidence, thereby promoting globally coherent surface reconstruction. Experiments on DTU show that CoSplat achieves state-of-the-art surface reconstruction accuracy among feed-forward Gaussian methods while retaining efficient inference. Zero-shot evaluations on DTU, BlendedMVS, and ETH3D further demonstrate strong cross-dataset generalization.