ArchShapeNet:An Interpretable 3D-CNN Framework for Evaluating Architectural Shapes

TL;DR

ArchShapeNet is an interpretable 3D-CNN framework that effectively classifies architectural forms, outperforming human experts and providing insights to improve generative design tools.

Contribution

The paper introduces ArchShapeNet, a novel 3D-CNN with a saliency module for analyzing architectural shapes, and presents a new dataset for form classification.

Findings

Model achieves 94.29% accuracy in form classification

Outperforms human experts in distinguishing form origins

Provides insights into differences between human-designed and machine-generated forms

Abstract

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.