GSRecon: Efficient Generalizable Gaussian Splatting for Surface Reconstruction from Sparse Views

Generalizable surface reconstruction aims to recover the surface the scene from a sparse set of images in a feedforward manner. Existing volume rendering-based methods evaluate numerous points along camera rays to infer the geometry, resulting in inefficient reconstruction. Recently, 3D Gaussian Splatting offers an alternative efficient scene representation and has inspired a series of surface reconstruction methods. However, these methods require dense views and cannot be generalized to new scenes. In this paper, we propose a novel surface reconstruction method with Gaussian splatting, named GSRecon, which leverages the advantages of rasterization-based rendering to achieve efficient reconstruction. To obtain accurate geometry representation, we propose a geometry-aware cross-view enhancement module to improve the unreliable geometry estimation in the current view by incorporating accurate geometric information from other views. To generate the fine-grained Gaussian primitives, we propose a hybrid cross-view feature aggregation module that integrates an efficient voxel branch and a fine-grained point branch to jointly capture cross-view geometric information. Subsequently, per-view depth maps are rendered using these Gaussian primitives and fused to obtain the final 3D surface. Extensive experiments on the DTU, BlendedMVS, and Tanks and Temples datasets validate that GSRecon achieves state-of-the-art performance efficiently. Code is available at https://github.com/hyangwinter/ GSRecon.