In the rapidly evolving field of 3D reconstruction, 3D Gaussian Splatting (3DGS) and 2D Gaussian Splatting (2DGS) represent significant advancements. Although 2DGS compresses 3D Gaussian primitives into 2D Gaussian surfels to effectively enhance mesh extraction quality, this compression can potentially lead to a decrease in rendering quality. Additionally, unreliable densification processes and the calculation of depth through the accumulation of opacity can compromise the detail of mesh extraction. To address this issue, we introduce MVG-Splatting, a solution guided by Multi-View considerations. Specifically, we integrate an optimized method for calculating normals, which, combined with image gradients, helps rectify inconsistencies in the original depth computations. Additionally, utilizing projection strategies akin to those in Multi-View Stereo (MVS), we propose an adaptive quantile-based method that dynamically determines the level of additional densification guided by depth maps, from coarse to fine detail. Experimental evidence demonstrates that our method not only resolves the issues of rendering quality degradation caused by depth discrepancies but also facilitates direct mesh extraction from dense Gaussian point clouds using the Marching Cubes algorithm. This approach significantly enhances the overall fidelity and accuracy of the 3D reconstruction process, ensuring that both the geometric details and visual quality.

翻译：在快速发展的三维重建领域，三维高斯溅射（3DGS）与二维高斯溅射（2DGS）代表了重要的技术进步。尽管2DGS将三维高斯基元压缩为二维高斯面元以有效提升网格提取质量，但这种压缩可能导致渲染质量下降。此外，不可靠的致密化过程以及通过不透明度累积计算深度的方法可能损害网格提取的细节。为解决此问题，我们提出了MVG-Splatting——一种受多视角引导的解决方案。具体而言，我们集成了一种优化的法向量计算方法，该方法结合图像梯度，有助于修正原始深度计算中的不一致性。同时，借鉴多视角立体视觉（MVS）中的投影策略，我们提出了一种基于自适应分位数的方法，能够根据深度图动态确定从粗到细的额外致密化程度。实验证明，我们的方法不仅解决了因深度差异导致的渲染质量下降问题，还能通过行进立方体算法直接从稠密高斯点云中提取网格。该方法显著提升了三维重建过程的整体保真度与精度，确保了几何细节与视觉质量的双重优化。