FoundSurface: Feedforward Ray-Based 3D Scene Surface Reconstruction from Unposed Images

Accurate 3D surface reconstruction from unposed images remains a fundamental challenge in computer vision. Recent feed-forward pointmap methods eliminate the need for camera poses but inherently produce discrete, sparse representations. In this paper, we present a generalizable pipeline that directly recovers continuous, high-fidelity 3D surfaces from unposed images by seamlessly integrating pointmap- and ray-based representations. Our approach first extracts explicit geometric priors using a pre-trained pointmap model. A query-view-centric selection module then identifies geometrically relevant reference views via a robust surfel voting mechanism. Next, a cross-attention regressor bridges target query rays with reference features to estimate a continuous coarse surface. Finally, a raylet-based module aggregates local 3D features to carve out high-frequency details and analytical normals. Trained as a single foundation model on 9 diverse datasets, our method demonstrates exceptional zero-shot generalization on 6 unseen datasets, clearly surpassing state-of-the-art baselines in geometric accuracy and detail preservation.