Prior-Driven Enhancements in 3D Gaussian Splatting: Normals and Depths Regularization

2026-09-29

AI Summary

This study addresses the geometric distortions and visual artifacts in 3D Gaussian Splatting (3DGS) caused by sparse point cloud initialization in complex scenes. To overcome the limitations of conventional Structure-from-Motion (SfM) sparse initialization, this work proposes a regularized optimization framework that integrates surface normals with dense depth priors. By aligning Gaussian covariances with local geometric structures, the proposed method effectively guides the 3DGS optimization process. Experimental results demonstrate that our approach significantly enhances both geometric accuracy and rendering quality in highly reflective, complex environments such as street scenes. Ultimately, this framework achieves reliable novel view synthesis that simultaneously preserves high fidelity and real-time performance.

Abstract

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 priors combined with an initial points from SfM enhance per-pixel depth estimation, increasing accuracy and reducing ambiguities. These enhancements enable robust handling of diverse and complex real-world scenarios, minimizing visual distortions and improving reconstruction quality across various environments. To validate our method, we evaluate it on challenging datasets, including street-view scenes and highly reflective environments, while testing it across multiple SfM pipelines. Our results demonstrate compatibility across diverse environments and highlight the robustness of our approach. Experimental findings further show that our method enhances geometric accuracy and visual quality, establishing a reliable solution for real-time 3D scene rendering in complex environments.

Problem

• 3D Gaussian Splatting
• geometric inaccuracies
• visual artifacts
• complex scenes
• 3D scene rendering

Innovation

• 3D Gaussian Splatting
• Surface Normal Regularization
• Dense Depth Prior
• Geometric Priors
• 3D Scene Rendering