3D Gaussian Splatting enables efficient optimization and high-quality rendering, yet accurate surface reconstruction remains challenging. Prior methods improve surface reconstruction by refining Gaussian depth estimates, either via multi-view geometric consistency or through monocular depth priors. However, multi-view constraints become unreliable under large geometric discrepancies, while monocular priors suffer from scale ambiguity and local inconsistency, ultimately leading to inaccurate Gaussian depth supervision. To address these limitations, we introduce a Gaussian visibility-aware multi-view geometric consistency constraint that aggregates the visibility of shared Gaussian primitives across views, enabling more accurate and stable geometric supervision. In addition, we propose a progressive quadtree-calibrated Monocular depth constraint that performs block-wise affine calibration from coarse to fine spatial scales, mitigating the scale ambiguity of depth priors while preserving fine-grained surface details. Extensive experiments on DTU and TNT datasets demonstrate consistent improvements in geometric accuracy over prior Gaussian-based and implicit surface reconstruction methods. Codes are available at an anonymous repository: https://github.com/GVGScode/GVGS.

翻译：三维高斯溅射技术实现了高效优化与高质量渲染，但精确的表面重建仍具挑战性。现有方法通过优化高斯深度估计来改进表面重建，其途径包括多视图几何一致性约束或单目深度先验。然而，多视图约束在几何差异较大时变得不可靠，而单目先验则受尺度模糊性与局部不一致性的影响，最终导致高斯深度监督不准确。为解决这些局限，我们提出了一种高斯可见性感知的多视图几何一致性约束，该约束聚合了共享高斯基元在多个视图中的可见性，从而实现更精确、更稳定的几何监督。此外，我们提出了一种渐进式四叉树校准的单目深度约束，该方法在从粗到细的空间尺度上执行分块仿射校准，在保留细粒度表面细节的同时缓解深度先验的尺度模糊性问题。在DTU和TNT数据集上的大量实验表明，相较于现有基于高斯的方法与隐式表面重建方法，本方法在几何精度上取得了持续提升。代码已发布于匿名仓库：https://github.com/GVGScode/GVGS。