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

Generalizable surface reconstruction aims to recover the scene surface from a sparse set of images in a feed-forward manner. Existing neural implicit representation-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 can not 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. Extensive experiments on the DTU, BlendedMVS, and Tanks and Temples datasets validate that GSRecon achieves state-of-the-art performance and efficient reconstruction speed.