GDoFS: Gaussian DoF Separation for Plausible 3D Geometry in Sparse-View 3DGS

Recent deep learning-based Multi-View Stereo (MVS) approaches, such as MASt3R and VGGT, have shown strong performance in sparse-view 3D reconstruction. However, refining these outputs with 3D Gaussian Splatting (3DGS) remains non-trivial. Excessive positional degrees of freedom (DoFs) in Gaussians often cause instability and geometric artifacts, sometimes distorting geometry to represent texture patterns. To address this issue, we propose GDoFS (Gaussian DoF Separation), a strategy that divides positional DoFs into two categories--image-plane-parallel and ray-aligned--based on their uncertainty. For each category, GDoFS introduces tailored optimization techniques, including bounded offsets for low-uncertainty DoFs and a visibility-guided loss for ray-aligned DoFs. Experiments on standard benchmarks demonstrate that GDoFS effectively mitigates geometric artifacts and produces reconstructions that are both visually coherent and structurally accurate.