GaussianEditor：基于高斯溅射的快速可控3D编辑
GaussianEditor: Swift and Controllable 3D Editing with Gaussian Splatting
AI总结：
提出基于高斯溅射的3D编辑算法GaussianEditor，通过高斯语义追踪和分层高斯溅射实现快速、可控的3D编辑，支持高效对象移除与集成。
摘要：
3D编辑在游戏和虚拟现实等许多领域扮演着关键角色。传统的3D编辑方法依赖于网格和点云等表示，通常在逼真描绘复杂场景方面表现不足。另一方面，基于隐式3D表示的方法，如神经辐射场（NeRF），能有效渲染复杂场景，但处理速度慢且对特定场景区域的控制有限。针对这些挑战，我们的论文提出了GaussianEditor，一种基于高斯溅射（GS）这一新型3D表示的高效创新3D编辑算法。GaussianEditor通过我们提出的高斯语义追踪增强了编辑的精度和控制，该追踪在整个训练过程中追踪编辑目标。此外，我们提出了分层高斯溅射（HGS），以在2D扩散模型的随机生成引导下实现稳定且精细的结果。我们还开发了编辑策略，用于高效的对象移除和集成，这是现有方法面临挑战的任务。我们的综合实验证明了GaussianEditor在控制性、有效性和快速性能方面的优越性，标志着3D编辑领域的重大进步。项目页面：https://buaacyw.github.io/gaussian-editor/
英文摘要：
3D editing plays a crucial role in many areas such as gaming and virtual reality. Traditional 3D editing methods, which rely on representations like meshes and point clouds, often fall short in realistically depicting complex scenes. On the other hand, methods based on implicit 3D representations, like Neural Radiance Field (NeRF), render complex scenes effectively but suffer from slow processing speeds and limited control over specific scene areas. In response to these challenges, our paper presents GaussianEditor, an innovative and efficient 3D editing algorithm based on Gaussian Splatting (GS), a novel 3D representation. GaussianEditor enhances precision and control in editing through our proposed Gaussian semantic tracing, which traces the editing target throughout the training process. Additionally, we propose Hierarchical Gaussian splatting (HGS) to achieve stabilized and fine results under stochastic generative guidance from 2D diffusion models. We also develop editing strategies for efficient object removal and integration, a challenging task for existing methods. Our comprehensive experiments demonstrate GaussianEditor's superior control, efficacy, and rapid performance, marking a significant advancement in 3D editing. Project Page: https://buaacyw.github.io/gaussian-editor/
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。
