ArchShapeNet:An Interpretable 3D-CNN Framework for Evaluating Architectural Shapes
This work addresses the problem of evaluating architectural shapes for designers and researchers, offering an interpretable tool to improve generative design tools, though it is incremental in applying existing methods to a new domain-specific dataset.
The study tackled the challenge of objectively analyzing differences between human-designed and machine-generated 3D architectural forms by building the ArchForms-4000 dataset and proposing ArchShapeNet, a 3D-CNN framework that achieved 94.29% accuracy in distinguishing form origins.
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.