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

2025-06-14

AI Summary

This study aims to objectively quantify morphological differences between human-designed and generative-AI-produced architectural 3D forms, thereby revealing domain-specific strengths and advancing design tool development. Method: We introduce ArchForms-4000—the first high-quality, morphology-oriented dataset for architectural form discrimination—and propose ArchShapeNet, an interpretable 3D-CNN architecture incorporating a Grad-CAM saliency module to automatically classify form origin and visualize architecturally meaningful spatial features. Contribution/Results: Our work provides the first systematic empirical evidence of human advantages in spatial organization, proportional harmony, and detail refinement. ArchShapeNet is the first interpretable 3D-CNN tailored for architectural morphology understanding. In form-origin classification, it achieves 94.29% accuracy, 96.2% precision, and 98.51% recall—significantly surpassing human expert performance.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Distinguish human-designed vs machine-generated 3D architectural forms
• Analyze spatial feature differences in architectural shapes objectively
• Improve generative design tools by identifying human design strengths

Innovation

• 3D-CNN for classifying architectural forms
• Saliency module highlights key spatial features
• Dataset with human and machine-generated forms