Recent learning-based Multi-View Stereo models have demonstrated state-of-the-art performance in sparse-view 3D reconstruction. However, directly applying 3D Gaussian Splatting (3DGS) as a refinement step following these models presents challenges. We hypothesize that the excessive positional degrees of freedom (DoFs) in Gaussians induce geometry distortion, fitting color patterns at the cost of structural fidelity. To address this, we propose reprojection-based DoF separation, a method distinguishing positional DoFs in terms of uncertainty: image-plane-parallel DoFs and ray-aligned DoF. To independently manage each DoF, we introduce a reprojection process along with tailored constraints for each DoF. Through experiments across various datasets, we confirm that separating the positional DoFs of Gaussians and applying targeted constraints effectively suppresses geometric artifacts, producing reconstruction results that are both visually and geometrically plausible.
近年来，基于学习的多视图立体（Multi-View Stereo, MVS）模型在稀疏视角的三维重建中表现出色。然而，将三维高斯点云（3D Gaussian Splatting, 3DGS）直接作为这些模型的后续优化步骤会面临一些挑战。我们假设，高斯基元中过多的位姿自由度（Degrees of Freedom, DoFs）会引发几何失真，导致为了匹配颜色模式而牺牲结构的准确性。 为了解决这一问题，我们提出了基于重投影的自由度分离（Reprojection-based DoF Separation）方法，通过不确定性将位姿自由度区分为图像平面平行自由度和沿射线方向的自由度。为了独立管理每种自由度，我们引入了一个重投影过程，并针对每种自由度设计了专门的约束。 在多个数据集上的实验结果表明，对高斯基元的位姿自由度进行分离并施加针对性的约束，能够有效抑制几何伪影，从而生成在视觉上和几何上均可信的重建结果。