用表面连续性先验提升稀疏图像3D重建精度，避免点云断裂和颜色偏差
SurfSplat: Conquering Feedforward 2D Gaussian Splatting with Surface Continuity Priors
- 基于2D高斯点云构建，引入表面连续性先验增强几何一致性
- 在真实场景数据集上实现更优的高分辨率重建质量，尤其在近景下表现显著
- 提出新评估指标HRRC，专用于衡量高分辨率下的重建保真度，适合追求细节的真实感重建者
从稀疏图像中重建三维场景仍具挑战性，主要难点在于无法通过优化恢复准确的几何与纹理。现有方法利用通用模型结合3D高斯点云（3DGS）原语生成3D场景，但常生成离散、色偏的点云，在正常视角下看似合理，近距离观察时暴露严重伪影。为此，我们提出SurfSplat，一种基于2D高斯点云（2DGS）原语的前馈框架，具备更强的方向性与更高几何精度。通过引入表面连续性先验与强制透明混合策略，SurfSplat实现了连贯几何与真实纹理的联合重建。此外，我们提出高分辨率渲染一致性（HRRC）这一新评估指标，专门用于衡量高分辨率重建质量。在RealEstate10K、DL3DV和ScanNet上的大量实验表明，SurfSplat在标准指标和HRRC上均持续优于现有方法，为稀疏输入下的高保真3D重建提供了稳健解决方案。
原文摘要 · Abstract (English)
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/
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