GaussEdit：基于文本和图像提示的自适应3D场景编辑
GaussEdit: Adaptive 3D Scene Editing with Text and Image Prompts
AI总结：
本研究提出GaussEdit框架，以3D高斯溅射为主干，通过三阶段流程结合文本与图像提示实现自适应3D场景编辑，在编辑精度、视觉保真度和速度上均优于现有方法。
摘要：
本文提出GaussEdit，一种由文本和图像提示引导的自适应3D场景编辑框架。GaussEdit以3D Gaussian Splatting（3D高斯溅射）作为场景表示的主干网络，通过三阶段流程实现便捷的感兴趣区域选择与高效编辑。第一阶段对3D高斯进行初始化，以保障编辑质量。第二阶段采用自适应全局-局部优化策略平衡全局场景一致性与局部细节编辑，并结合类别引导的正则化技术缓解Janus（双面）问题。最后阶段通过精细的图像到图像合成技术增强编辑对象的纹理，确保结果视觉真实且与给定提示高度契合。实验结果表明，GaussEdit在编辑精度、视觉保真度和处理速度上均优于现有方法。通过将用户指定概念成功嵌入3D场景，GaussEdit成为实现精细化、用户驱动3D场景编辑的有力工具，较传统方法有显著提升。
英文摘要：
This paper presents GaussEdit, a framework for adaptive 3D scene editing guided by text and image prompts. GaussEdit leverages 3D Gaussian Splatting as its backbone for scene representation, enabling convenient Region of Interest selection and efficient editing through a three-stage process. The first stage involves initializing the 3D Gaussians to ensure high-quality edits. The second stage employs an Adaptive Global-Local Optimization strategy to balance global scene coherence and detailed local edits and a category-guided regularization technique to alleviate the Janus problem. The final stage enhances the texture of the edited objects using a sophisticated image-to-image synthesis technique, ensuring that the results are visually realistic and align closely with the given prompts. Our experimental results demonstrate that GaussEdit surpasses existing methods in editing accuracy, visual fidelity, and processing speed. By successfully embedding user-specified concepts into 3D scenes, GaussEdit is a powerful tool for detailed and user-driven 3D scene editing, offering significant improvements over traditional methods.
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。
