Prior-Driven Enhancements in 3D Gaussian Splatting: Normals and Depths Regularization
3D Gaussian Splatting (3DGS) is a state-of-the-art technique for 3D scene rendering, offering high efficiency and excellent visual quality. However, because 3DGS relies on an initial sparse point set from Structure-from-Motion (SfM) and view-dependent properties, it can suffer from geometric inaccuracies and visual artifacts, particularly in complex scenes. To address these challenges, we propose an improved 3DGS approach that regularizes the optimization process by integrating geometric priors, including surface normals and dense depth information. Surface normal regularization improves geometric consistency by aligning Gaussian covariance with local surface structures, while dense depth...
| Authors | Gyeonggwan Lee, Seunghwan Hong, Junghun Suh |
|---|---|
| Primary category | cs.CV |
| Published | 29 September 2026 |
| Last updated at source | 29 September 2026 |
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