PolyLayout：超越矩形房间的分层VLM引导布局生成

中文摘要 AI 辅助

生成物理上合理的三维房间布局对于家居零售至关重要，能让客户在自家环境中可视化产品并自信做出购买决策。然而，学术研究与实际应用之间存在差距：现有解决方案主要关注家具摆放的算法策略，在很大程度上忽略了真实住宅中普遍存在的非矩形几何形状以及严格的门窗约束。为缩小这一差距，我们引入了一种专为零售场景设计的混合分层框架，专门用于支持可扩展的空间规划应用。我们的系统将生成过程拆分为三个阶段：（1）功能家具聚类与区域内精细摆放；（2）由视觉语言模型（VLM）引导的宏观路径规划，以在多样的多边形边界内锚定这些聚类区域及任何剩余的独立家具；（3）基于规则的优化，以实现符合建筑约束的无碰撞微布局。我们在生产规模的产品目录和一组具有代表性的不规则真实拓扑结构上对系统进行评估。结果表明，我们的方法在保持良好几何合规性的同时，实现了最高的感知合理性，且延迟相对较低，还能扩展到现有方法无法原生支持的不规则边界。

英文摘要

Generating physically plausible 3D room layouts is essential for home furnishing retail, enabling customers to visualize products in their own homes and confidently make purchasing decisions. However, a gap exists between academic research and real-world application: existing solutions primarily focus on algorithmic strategies for furniture placement, largely neglecting the non-rectangular geometries and strict door/window constraints prevalent in real homes. To bridge the gap, we introduce a hybrid, hierarchical framework tailored for retail, specifically designed to support scalable spatial planning applications. Our system decouples generation into three stages: (1) functional furniture clustering and fine-grained intra-zone placement; (2) macro-routing guided by a vision-language model (VLM) to anchor both these clustered zones and any remaining standalone furniture within diverse polygonal boundaries; and (3) rule-based optimization for collision-free micro-arrangements that respect architectural constraints. We evaluate our system on production-scale catalogs and a representative set of irregular real-world topologies. Our results show that our approach attains the highest perceptual plausibility while maintaining good geometric compliance at relatively low latency, and extends to irregular boundaries that existing methods do not natively support.

发表机构

• IKEA Retail (Ingka Group)（宜家零售（英卡集团））

机构由 AI 辅助整理，请以论文原文为准。