用AI区分人与机器设计的建筑形态，准确率达94%
ArchShapeNet:An Interpretable 3D-CNN Framework for Evaluating Architectural Shapes
- 构建3D CNN模型ArchShapeNet，融合注意力机制识别关键空间特征
- 在4000个建筑形态上实现94.29%分类准确率，超越人类专家
- 揭示人工设计在比例与细节上的优势，助力生成工具优化
当前建筑设计日益复杂多样，生成式插件工具对快速生成概念和探索新三维形式至关重要。然而，客观分析人工设计与机器生成三维形态的差异仍具挑战，限制了对其优劣的理解，也阻碍了生成工具的发展。为此，我们构建了包含2000个建筑师设计和2000个Evomass生成的三维形态的ArchForms-4000数据集；提出专用于建筑形态分类与分析的3D卷积神经网络ArchShapeNet，引入显著性模块以突出符合建筑思维的关键空间特征；并通过对比实验表明，该模型在区分形态来源方面优于人类专家，达到94.29%准确率、96.2%精确率和98.51%召回率。本研究不仅凸显了人工设计在空间组织、比例和谐与细节精修方面的优势，也为未来提升生成设计工具提供了宝贵洞见。
原文摘要 · Abstract (English)
In contemporary architectural design, the growing complexity and diversity of design demands have made generative plugin tools essential for quickly producing initial concepts and exploring novel 3D forms. However, objectively analyzing the differences between human-designed and machine-generated 3D forms remains a challenge, limiting our understanding of their respective strengths and hindering the advancement of generative tools. To address this, we built ArchForms-4000, a dataset containing 2,000 architect-designed and 2,000 Evomass-generated 3D forms; Proposed ArchShapeNet, a 3D convolutional neural network tailored for classifying and analyzing architectural forms, incorporating a saliency module to highlight key spatial features aligned with architectural reasoning; And conducted comparative experiments showing our model outperforms human experts in distinguishing form origins, achieving 94.29% accuracy, 96.2% precision, and 98.51% recall. This study not only highlights the distinctive advantages of human-designed forms in spatial organization, proportional harmony, and detail refinement but also provides valuable insights for enhancing generative design tools in the future.
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