Recent work on image content manipulation based on vision-language pre-training models has been effectively extended to text-driven 3D scene editing. However, existing schemes for 3D scene editing still exhibit certain shortcomings, hindering their further interactive design. Such schemes typically adhere to fixed input patterns, limiting users' flexibility in text input. Moreover, their editing capabilities are constrained by a single or a few 2D visual models and require intricate pipeline design to integrate these models into 3D reconstruction processes. To address the aforementioned issues, we propose a dialogue-based 3D scene editing approach, termed CE3D, which is centered around a large language model that allows for arbitrary textual input from users and interprets their intentions, subsequently facilitating the autonomous invocation of the corresponding visual expert models. Furthermore, we design a scheme utilizing Hash-Atlas to represent 3D scene views, which transfers the editing of 3D scenes onto 2D atlas images. This design achieves complete decoupling between the 2D editing and 3D reconstruction processes, enabling CE3D to flexibly integrate a wide range of existing 2D or 3D visual models without necessitating intricate fusion designs. Experimental results demonstrate that CE3D effectively integrates multiple visual models to achieve diverse editing visual effects, possessing strong scene comprehension and multi-round dialog capabilities. Code is available at <a href="https://sk-fun.fun/CE3D"> this https URL.</a>

基于视觉语言预训练模型的图像内容操作已成功扩展到以文本驱动的3D场景编辑。本文提出一种基于对话的3D场景编辑方法CE3D，以大型语言模型为中心，允许用户进行任意文本输入并解析其意图，进而自主调用相应的视觉专家模型。通过设计利用Hash-Atlas来表示3D场景视图的方案，将3D场景编辑转移到2D图像上，实现了2D编辑和3D重建过程的完全解耦，使CE3D能够灵活地整合各种现有的2D或3D视觉模型，而不需要复杂的融合设计。实验结果表明，CE3D能够有效地整合多个视觉模型，实现多样的编辑视觉效果，并具备强大的场景理解能力和多轮对话功能。

Chat-Edit-3D: 通过文本提示的交互式3D场景编辑