Generating complete 3D scenes from a single image requires inferring globally consistent geometry, object relationships, and environmental context from inherently ambiguous visual evidence. Despite recent progress in joint layout-and-mesh generation, existing methods often rely on holistic or weakly decomposed pipelines that entangle many factors at once and demand extensive scene-level supervision, limiting their generalization to complex real-world environments. We propose a multi-agent orchestration framework that decomposes single-image 3D scene generation into three structured stages: scene initialization, environment construction, and multi-agent refinement. The initialization stage extracts image-derived object masks, builds object-level 3D representations, and predicts an initial spatial layout to form a coarse 3D scene. The environment-construction stage then leverages this initialization together with point-map geometry to build an environmental scaffold of supporting surfaces, room boundaries, materials, and illumination. Finally, in the refinement stage, a planner agent identifies structural and visual inconsistencies, applies simple corrections directly, and dispatches specialist agents for complex localized revisions that are reintegrated into the global scene. To provide reliable structural initialization while reducing reliance on scene-level annotations, we further introduce a geometry-aware layout predictor supervised by sparse geometric priors derived from point maps. Unlike fully supervised layout generators, the predictor can be trained from segmentation-level data and generalizes robustly to diverse real-world scenes. Extensive experiments on benchmark datasets show that our method consistently outperforms prior approaches in geometric accuracy, spatial consistency, and perceptual realism.

翻译：从单张图像生成完整的三维场景，需要从本质上存在歧义的视觉证据中推断全局一致的几何结构、物体关系及环境上下文。尽管近期在联合布局与网格生成方面取得进展，现有方法通常依赖整体式或弱分解式流水线，该流水线同时纠缠多个因素并需要大量场景级标注，限制了其在复杂真实环境中的泛化能力。我们提出一种多智能体编排框架，将单图像三维场景生成分解为三个结构化阶段：场景初始化、环境构建与多智能体精化。初始化阶段提取源自图像的物体掩膜、构建物体级三维表征，并预测初始空间布局以形成粗糙的三维场景。环境构建阶段则利用该初始化结果与点图几何信息，构建支撑表面、房间边界、材质与光照的环境骨架。最终，在精化阶段，规划器智能体识别结构与视觉不一致性，直接进行简单修正，并派遣专家智能体执行复杂局部调整后将其重新集成至全局场景。为在降低场景级标注依赖的同时提供可靠的结构初始化，我们进一步引入一种几何感知布局预测器，该预测器由源自点图的稀疏几何先验监督。与完全监督的布局生成器不同，该预测器可通过分割级数据训练，并鲁棒地泛化至多样化真实场景。在基准数据集上的大量实验表明，我们的方法在几何精度、空间一致性与感知真实感方面持续优于先前方法。