Reconstructing 3D scenes from sparse images remains a challenging task due to the difficulty of recovering accurate geometry and texture without optimization. Recent approaches leverage generalizable models to generate 3D scenes using 3D Gaussian Splatting (3DGS) primitive. However, they often fail to produce continuous surfaces and instead yield discrete, color-biased point clouds that appear plausible at normal resolution but reveal severe artifacts under close-up views. To address this issue, we present SurfSplat, a feedforward framework based on 2D Gaussian Splatting (2DGS) primitive, which provides stronger anisotropy and higher geometric precision. By incorporating a surface continuity prior and a forced alpha blending strategy, SurfSplat reconstructs coherent geometry together with faithful textures. Furthermore, we introduce High-Resolution Rendering Consistency (HRRC), a new evaluation metric designed to evaluate high-resolution reconstruction quality. Extensive experiments on RealEstate10K, DL3DV, and ScanNet demonstrate that SurfSplat consistently outperforms prior methods on both standard metrics and HRRC, establishing a robust solution for high-fidelity 3D reconstruction from sparse inputs. Project page: https://hebing-sjtu.github.io/SurfSplat-website/

翻译：从稀疏图像重建三维场景仍然是一项具有挑战性的任务，原因在于难以在不进行优化的情况下恢复精确的几何结构和纹理。最近的方法利用泛化模型，通过三维高斯泼溅（3DGS）基元来生成三维场景。然而，这些方法通常无法生成连续的表面，而是产生离散的、带有颜色偏差的点云，这些点云在正常分辨率下看似合理，但在近距离观察下会暴露出严重的伪影。为了解决这个问题，我们提出了SurfSplat，一个基于二维高斯泼溅（2DGS）基元的前馈框架，该框架提供了更强的各向异性和更高的几何精度。通过引入表面连续性先验和强制阿尔法混合策略，SurfSplat能够重建连贯的几何结构以及逼真的纹理。此外，我们提出了高分辨率渲染一致性（HRRC），这是一种新的评估指标，旨在评估高分辨率重建的质量。在RealEstate10K、DL3DV和ScanNet数据集上进行的大量实验表明，SurfSplat在标准指标和HRRC上均持续优于先前的方法，为从稀疏输入进行高保真三维重建提供了一个稳健的解决方案。项目页面：https://hebing-sjtu.github.io/SurfSplat-website/