3D Gaussian Splatting has achieved impressive performance in novel view synthesis with real-time rendering capabilities. However, reconstructing high-quality surfaces with fine details using 3D Gaussians remains a challenging task. In this work, we introduce GausSurf, a novel approach to high-quality surface reconstruction by employing geometry guidance from multi-view consistency in texture-rich areas and normal priors in texture-less areas of a scene. We observe that a scene can be mainly divided into two primary regions: 1) texture-rich and 2) texture-less areas. To enforce multi-view consistency at texture-rich areas, we enhance the reconstruction quality by incorporating a traditional patch-match based Multi-View Stereo (MVS) approach to guide the geometry optimization in an iterative scheme. This scheme allows for mutual reinforcement between the optimization of Gaussians and patch-match refinement, which significantly improves the reconstruction results and accelerates the training process. Meanwhile, for the texture-less areas, we leverage normal priors from a pre-trained normal estimation model to guide optimization. Extensive experiments on the DTU and Tanks and Temples datasets demonstrate that our method surpasses state-of-the-art methods in terms of reconstruction quality and computation time.
3D 高斯点云表示（3D Gaussian Splatting）在新视图合成和实时渲染中表现出色，但在高质量、细节丰富的表面重建方面仍然具有挑战性。为此，我们提出了一种新方法 GausSurf，通过在纹理丰富区域利用多视图一致性几何指导，以及在纹理缺乏区域利用法向量先验，实现高质量的表面重建。 我们观察到一个场景主要可以划分为两个区域：1）纹理丰富区域和 2）纹理缺乏区域。针对纹理丰富区域，我们通过结合传统的基于补丁匹配的多视图立体（Multi-View Stereo, MVS）方法，在迭代优化方案中引导几何优化，增强重建质量。该方案允许高斯点优化与补丁匹配细化之间的相互增强，从而显著提升重建结果并加速训练过程。 而针对纹理缺乏区域，我们从预训练的法向量估计模型中提取法向量先验，用于指导优化。通过这种结合纹理区域特性的双重优化策略，GausSurf 能够更准确地恢复复杂场景中的几何细节。 在 DTU 和 Tanks and Temples 数据集上的广泛实验表明，GausSurf 在重建质量和计算时间上均优于当前最先进的方法，展现了其卓越的性能和高效性。