MVG-Splatting: Multi-View Guided Gaussian Splatting with Adaptive Quantile-Based Geometric Consistency Densification
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.
在快速发展的3D重建领域中，3D高斯喷溅（3DGS）和2D高斯喷溅（2DGS）代表了重大进展。尽管2DGS将3D高斯原始体压缩为2D高斯面元，有效地提高了网格提取质量，但这种压缩可能会导致渲染质量下降。此外，不可靠的增密过程和通过不透明度累积计算深度可能会损害网格提取的细节。为解决这一问题，我们引入了MVG喷溅，这是一种受多视角考虑指导的解决方案。具体来说，我们整合了一种优化的法线计算方法，结合图像梯度，帮助纠正原始深度计算中的不一致。此外，利用类似于多视图立体（MVS）中的投影策略，我们提出了一种自适应的分位数基方法，动态确定从粗糙到细节的额外增密级别，该级别由深度图指导。实验证据表明，我们的方法不仅解决了由深度差异引起的渲染质量降低的问题，而且还通过使用Marching Cubes算法直接从密集的高斯点云中提取网格，显著提高了整个3D重建过程的整体保真度和准确性，确保了几何细节和视觉质量。