Depth-Gradient-Guided Decoupled Optimization in 3D Gaussian Splatting for Sparse-View Reconstruction

3D Gaussian Splatting (3DGS) achieves high-quality novel-view synthesis under dense inputs but suffers from severe overfitting and geometric artifacts in sparse-view settings. Existing approaches introduce monocular depth priors to alleviate this issue, yet they typically apply uniform geometric constraints across the entire scene, ignoring structural heterogeneity between flat and edge regions, which leads to a trade-off between global smoothness and local detail preservation. To address this limitation, we propose Depth-Gradient-Guided Decoupled Optimization for Gaussian Splatting (DGDO-GS), a geometry-aware framework designed for sparse-view reconstruction. Specifically, depth gradients are exploited to capture structural variations and partition the scene into flat and edge regions. Global consistency constraints are imposed on flat areas to enhance stability, while local sharpening regularization is applied to edge regions to preserve fine-grained details. An orthogonal decoupling strategy further separates position and shape optimization, enabling accurate geometric recovery under monocular depth supervision while maintaining high-frequency texture fidelity. Experiments on LLFF, Mip-NeRF360, and Blender demonstrate that DGDO-GS consistently outperforms existing methods in reconstruction quality.