GDGS：通过几何引导初始化和动态密度控制的3D高斯泼溅
GDGS: 3D Gaussian Splatting Via Geometry-Guided Initialization And Dynamic Density Control
AI总结：
本文提出GDGS方法，通过几何引导初始化、表面对齐优化和动态自适应密度控制三项创新，解决了3D高斯泼溅在初始化、优化和密度控制方面的挑战，实现了复杂场景下的高保真实时渲染。
摘要：
我们提出了一种增强3D高斯泼溅（3DGS）的方法，以解决其在初始化、优化和密度控制方面的挑战。高斯泼溅是一种在支持实时性能的同时渲染逼真图像的替代方案，由于其显式的3D高斯表示而受到广泛关注。然而，3DGS严重依赖准确的初始化，并且在将无结构的高斯分布优化为有序表面方面面临困难，目前提出的自适应密度控制机制也较为有限。我们的第一个关键贡献是一种几何引导的初始化方法，用于预测高斯参数，确保精确放置并加快收敛速度。随后，我们引入了一种表面对齐的优化策略来细化高斯放置，提高几何精度并与场景的表面法线对齐。最后，我们提出了一种动态自适应密度控制机制，根据区域复杂度调整高斯密度，以提升视觉保真度。这些创新使我们的方法能够实现高保真实时渲染，并在复杂场景中显著改善视觉质量。我们的方法展现出与最先进方法相当或更优的结果，能够实时渲染高保真图像。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
