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.

翻译：三维高斯喷溅（3DGS）已成为实时生成场景逼真渲染的强大技术。然而，3DGS的体积特性限制了其精确捕捉表面几何的能力。为此，二维高斯喷溅（2DGS）被提出，以实现从多视图图像进行视角一致且几何精确的表面重建。然而，2DGS对高斯基元的初始化较为敏感。若依赖运动恢复结构（SfM）初始化——其在具有挑战性的图像集上可能产生较差的估计——则可能导致次优结果。在本工作中，我们通过引入单目深度和法向先验来增强2DGS，从而提升几何精度与鲁棒性。我们提出了一种深度引导的高斯初始化策略，并引入基于聚类的退化高斯修剪技术。我们在DTU数据集上评估了本方法，在网格重建中达到了最优结果，同时保持了高质量的新视角合成性能。