用智能框架自动生成节能合规的建筑平面图，省时省力。
GreenPlanner: Practical Floorplan Layout Generation via an Energy-Aware and Function-Feasible Generative Framework
- 通过学习约束先验和快速评估器，统一生成与评价
- 评估速度提升10万倍，准确率超99%，无效方案归零
- 适合建筑师和设计师，大幅提升设计效率
建筑布局直接影响人类福祉和碳排放，但生成空间功能合理且符合能耗规范的平面图仍依赖人工，成本高且难以扩展。现有方法虽能生成视觉上合理的布局，却常违反关键约束，因缺乏自动化评估导致结果无效。我们提出GreenPlanner，一种兼顾能源与功能可行性的生成框架，包含：用于学习约束先验的标注设计可行性数据集；可预测能耗表现与空间功能有效性的快速实用设计评估器（PDE）；基于PDE引导筛选生成的绿色平面数据集（GreenPD），将用户需求与合规布局配对；以及在GreenPD上训练并接受PDE反馈的GreenFlow生成器，实现可控、合规的生成。实验表明，GreenPlanner使评估速度提升超过10⁵倍，准确率高于99%，消除无效样本，并使设计效率比专业建筑师提高87%。
原文摘要 · Abstract (English)
Building design directly affects human well-being and carbon emissions, yet generating spatial-functional and energy-compliant floorplans remains manual, costly, and non-scalable. Existing methods produce visually plausible layouts but frequently violate key constraints, yielding invalid results due to the absence of automated evaluation. We present GreenPlanner, an energy- and functionality-aware generative framework that unifies design evaluation and generation. It consists of a labeled Design Feasibility Dataset for learning constraint priors; a fast Practical Design Evaluator (PDE) for predicting energy performance and spatial-functional validity; a Green Plan Dataset (GreenPD) derived from PDE-guided filtering to pair user requirements with regulation-compliant layouts; and a GreenFlow generator trained on GreenPD with PDE feedback for controllable, regulation-aware generation. Experiments show that GreenPlanner accelerates evaluation by over $10^{5}\times$ with $>$99% accuracy, eliminates invalid samples, and boosts design efficiency by 87% over professional architects.
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