AI 中文总结
针对现有室内布局生成器的局部违规问题，提出Roomer框架，通过视觉语言模型规划器与确定性求解器实现基于对象的局部修复，提升布局物理有效性与可用性且可跨生成器迁移。
AI 中文摘要
现有室内布局生成器生成的布局全局合理，但可能存在局部违规，如碰撞、超出边界放置、开口被遮挡、通行受阻。多数现有工作聚焦全场景合成或场景级优化，对识别责任对象及局部修复受影响区域的支持有限。我们提出Roomer，一个反射式修复框架，将这些违规视为稀疏的、基于对象的修复问题。Roomer将布局编码为“RoState”，并使用“RoReview”将测量的违规与相关对象绑定。一个几何条件的视觉语言模型规划器提出结构化局部编辑，而确定性求解器会验证该编辑，必要时生成有限的候选编辑集。仅当全场景验证确认候选编辑解决了目标违规且未产生新的硬性违规或破坏受保护约束时，才会采纳该候选编辑。我们在Roomer-CC上训练规划器，这是一个受控损坏数据集，将有缺陷的布局与基于对象的违规证据及已知可行的逆StatePatches配对。由于现有基准很少评估物理上有效的布局是否可用，我们引入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了残留违规，提升了物理有效性和可用性，且可跨外部生成器迁移。
英文摘要
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.
