G-Style：风格化高斯泼溅
G-Style: Stylized Gaussian Splatting
AI总结：
本文提出 G-Style 算法，通过预处理剔除不良高斯、多尺度风格损失和基于颜色梯度的自适应高斯分裂，实现快速且高质量的三维高斯泼溅场景图像风格迁移。
摘要：
我们提出 G-Style，一种新颖的算法，旨在将图像的风格迁移到使用高斯泼溅（Gaussian Splatting）表示的三维场景上。高斯泼溅是一种强大的三维表示方法，用于新视角合成，因为与基于神经辐射场（Neural Radiance Fields）的其他方法相比，它能够提供快速的场景渲染以及用户对场景的控制。近期的预印本已经证明，高斯泼溅场景的风格可以通过图像样例进行修改。然而，由于在风格化过程中场景几何保持固定，现有解决方案难以产生令人满意的结果。我们的算法旨在通过三步流程来解决这些局限：在预处理步骤中，我们移除具有较大投影面积或高度拉长形状的不理想高斯。随后，我们组合多个精心设计的损失函数，以保留图像中不同尺度的风格，同时尽可能保持原始场景内容的完整性。在风格化过程中，遵循高斯泼溅的原始设计，我们通过跟踪风格化颜色的梯度，在场景中需要额外细节的位置分裂高斯。我们的实验表明，G-Style 能在短短几分钟内生成高质量的风格化结果，在定性和定量两方面均优于现有方法。
英文摘要：
We introduce G-Style, a novel algorithm designed to transfer the style of an image onto a 3D scene represented using Gaussian Splatting. Gaussian Splatting is a powerful 3D representation for novel view synthesis, as -- compared to other approaches based on Neural Radiance Fields -- it provides fast scene renderings and user control over the scene. Recent pre-prints have demonstrated that the style of Gaussian Splatting scenes can be modified using an image exemplar. However, since the scene geometry remains fixed during the stylization process, current solutions fall short of producing satisfactory results. Our algorithm aims to address these limitations by following a three-step process: In a pre-processing step, we remove undesirable Gaussians with large projection areas or highly elongated shapes. Subsequently, we combine several losses carefully designed to preserve different scales of the style in the image, while maintaining as much as possible the integrity of the original scene content. During the stylization process and following the original design of Gaussian Splatting, we split Gaussians where additional detail is necessary within our scene by tracking the gradient of the stylized color. Our experiments demonstrate that G-Style generates high-quality stylizations within just a few minutes, outperforming existing methods both qualitatively and quantitatively.
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。
