用深度和法向先验改进2D高斯点云，让3D重建更准更稳。
2D-SuGaR: Surface-Aware Gaussian Splatting for Geometrically Accurate Mesh Reconstruction
- 用单目深度和法向引导高斯点初始化，提升几何精度
- 在DTU数据集上实现最优网格重建效果，新视角合成质量高
- 适合需要高保真3D重建的工业应用与视觉建模任务
3D高斯点阵（3DGS）虽能实现实时逼真渲染，但其体素特性限制了表面几何的精确捕捉。为此提出2D高斯点阵（2DGS），可从多视角图像中实现视图一致且几何准确的表面重建。然而，2DGS对高斯原始点的初始化敏感，依赖结构光（SfM）初始估计时，在复杂图像集上易产生劣质结果。本文通过引入单目深度和法向先验，增强2DGS的几何准确性和鲁棒性。提出深度引导的高斯初始化策略，并设计基于聚类的退化高斯点剔除方法。在DTU数据集上评估表明，本方法在网格重建上达到当前最优水平，同时保持高质量的新视角合成效果。
原文摘要 · Abstract (English)
3D Gaussian Splatting (3DGS) has emerged as a powerful technique for generating photorealistic renderings of a scene in real-time. However, the volumetric nature of 3DGS limits its ability to accurately capture surface geometry. To address this, 2D Gaussian Splatting (2DGS) was proposed to enable view-consistent and geometrically accurate surface reconstruction from multi-view images. However, 2DGS can be sensitive to the initialization of the Gaussian primitives. Reliance on Structure-from-Motion (SfM) initializations, which can produce poor estimates on challenging image sets, may lead to subpar results. In this work, we enhance 2DGS by incorporating monocular depth and normal priors to improve both geometric accuracy and robustness. We propose a depth-guided initialization strategy for Gaussians and introduce a clustering-based technique for pruning degenerate Gaussians. We evaluate our method on the DTU dataset, where it achieves state-of-the-art results in mesh reconstruction while preserving high-quality novel view synthesis.
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