通过几何引导初始化与动态密度控制，提升3D高斯点云的渲染质量与效率。
GDGS: 3D Gaussian Splatting Via Geometry-Guided Initialization And Dynamic Density Control
- 用几何信息指导高斯点初始分布，加速收敛。
- 根据区域复杂度动态调整密度，提升细节表现力。
- 适合需要实时高质量渲染的三维重建场景。
我们提出一种增强3D高斯点云（3DGS）的方法，解决初始化、优化和密度控制难题。3DGS因其显式高斯表示和实时渲染能力而广受欢迎，但其性能高度依赖精确初始化，且难以将无序高斯分布优化为有序表面，现有自适应密度控制机制有限。本文首项贡献为几何引导初始化，可预测高斯参数，确保精准定位并加快收敛。其次提出面向表面的优化策略，精修高斯位置，提升几何精度并贴合场景法向。最后设计动态自适应密度控制机制，依据区域复杂度调节密度以保障视觉保真度。实验表明，该方法在复杂场景中实现高保真实时渲染，性能达到或优于当前最优方法。
原文摘要 · Abstract (English)
We propose a method to enhance 3D Gaussian Splatting (3DGS)~\cite{Kerbl2023}, addressing challenges in initialization, optimization, and density control. Gaussian Splatting is an alternative for rendering realistic images while supporting real-time performance, and it has gained popularity due to its explicit 3D Gaussian representation. However, 3DGS heavily depends on accurate initialization and faces difficulties in optimizing unstructured Gaussian distributions into ordered surfaces, with limited adaptive density control mechanism proposed so far. Our first key contribution is a geometry-guided initialization to predict Gaussian parameters, ensuring precise placement and faster convergence. We then introduce a surface-aligned optimization strategy to refine Gaussian placement, improving geometric accuracy and aligning with the surface normals of the scene. Finally, we present a dynamic adaptive density control mechanism that adjusts Gaussian density based on regional complexity, for visual fidelity. These innovations enable our method to achieve high-fidelity real-time rendering and significant improvements in visual quality, even in complex scenes. Our method demonstrates comparable or superior results to state-of-the-art methods, rendering high-fidelity images in real time.
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