Roomer：面向三维室内布局生成的反思性基于对象的模型编辑与修复

摘要

现有的室内布局生成器生成的布局在整体上看似合理，但仍可能存在局部违规，如碰撞、越界放置、开口受阻和动线堵塞。以往的大多数工作侧重于全场景合成或场景级优化，对识别责任对象和局部修复受影响区域的支持有限。我们提出了 Roomer，一个反思式修复框架，将这些违规视为稀疏的、以对象为基底的修复问题。Roomer 将布局编码为“RoState”，并使用“RoReview”将测量到的违规与相关对象绑定。一个几何条件视觉语言模型规划器提出结构化的局部编辑，而一个确定性求解器对其进行验证，并在需要时生成有限数量的候选编辑。每个候选只有在全场景验证确认其解决了目标违规且未引入新的硬违规或破坏受保护约束时才会被采用。我们在 Roomer-CC（一个受控损坏数据集）上训练规划器，该数据集将故障布局与对象级违规证据以及已知可行的逆状态补丁配对。由于现有基准很少评估物理有效的布局是否可用，我们引入了 Roomer-Eval 来评估分布质量、物理有效性和实际可用性。实验表明，Roomer 在保留有效区域的同时修复残余违规，提高物理有效性和可用性，并能跨外部生成器迁移。

English

Existing indoor layout generators produce globally plausible layouts yet may retain local violations such as collisions, out-of-bounds placements, obstructed openings, and blocked circulation. Most prior work focuses on full-scene synthesis or scene-level optimization, with limited support for identifying responsible objects and locally repairing affected regions. We present Roomer, a reflective repair framework that casts these violations as sparse, object-grounded repair problems. Roomer encodes layouts as ``RoState'' and uses ``RoReview'' to bind measured violations to implicated objects. A geometry-conditioned vision-language model planner proposes a structured local edit, while a deterministic solver validates it and generates a finite set of candidate edits when needed. Each candidate is committed only if full-scene verification confirms that it resolves the target violation without new hard violations or broken protected constraints. We train the planner on Roomer-CC, a controlled-corruption dataset that pairs faulty layouts with object-grounded violation evidence and known-feasible inverse StatePatches. Since existing benchmarks rarely assess whether physically valid layouts are usable, we introduce Roomer-Eval to assess distributional quality, physical validity, and practical usability. Experiments show that Roomer repairs residual violations while preserving valid regions, improves physical validity and usability, and transfers across external generators.