DisCo-Layout：在用于3D室内布局合成的多智能体框架中解耦并协调语义与物理优化
DisCo-Layout: Disentangling and Coordinating Semantic and Physical Refinement in a Multi-Agent Framework for 3D Indoor Layout Synthesis
AI总结：
针对3D室内布局合成现有方法泛化性差、优化不足的问题，提出DisCo-Layout多智能体框架，解耦协调语义与物理优化，实现了当前最优的布局生成效果。
摘要：
3D室内布局合成对构建虚拟环境至关重要。传统方法受限于固定数据集，泛化能力不足。近期基于大语言模型（LLM）和视觉语言模型（VLM）的方法虽提升了语义丰富度，但通常缺乏稳健灵活的优化机制，导致布局效果欠佳。我们提出DisCo-Layout，这是一种解耦并协调物理与语义优化的新型框架。在独立优化阶段，语义优化工具（SRT）修正抽象的对象关系，物理优化工具（PRT）通过网格匹配算法解决具体的空间问题。在协同优化阶段，多智能体框架智能调度这些工具，包含负责布局规则的规划器、生成初始布局的设计器和执行评估的评估器。实验表明DisCo-Layout达到了当前最优性能，可生成真实、连贯且泛化性强的3D室内布局，我们的代码将公开。
英文摘要：
3D indoor layout synthesis is crucial for creating virtual environments. Traditional methods struggle with generalization due to fixed datasets. While recent LLM and VLM-based approaches offer improved semantic richness, they often lack robust and flexible refinement, resulting in suboptimal layouts. We develop DisCo-Layout, a novel framework that disentangles and coordinates physical and semantic refinement. For independent refinement, our Semantic Refinement Tool (SRT) corrects abstract object relationships, while the Physical Refinement Tool (PRT) resolves concrete spatial issues via a grid-matching algorithm. For collaborative refinement, a multi-agent framework intelligently orchestrates these tools, featuring a planner for placement rules, a designer for initial layouts, and an evaluator for assessment. Experiments demonstrate DisCo-Layout's state-of-the-art performance, generating realistic, coherent, and generalizable 3D indoor layouts. Our code will be publicly available.
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。
