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

arXiv: https://arxiv.org/abs/2506.14832 · HTML: https://mlsanity.com/paper/2506.14832/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 31 of its 86 references that are in the map.
- First posted on arXiv 14 Jun 2025.

## Builds on (most cited first, 30 of 31)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://mlsanity.com/paper/1709.00029/) (2017)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation](https://mlsanity.com/paper/1603.05959/) (2016)
- [M3D-RPN: Monocular 3D Region Proposal Network for Object Detection](https://mlsanity.com/paper/1907.06038/) (2019)
- [Interpretable Convolutional Neural Networks](https://mlsanity.com/paper/1710.00935/) (2017)
- [3D Deep Learning on Medical Images: A Review](https://mlsanity.com/paper/2004.00218/) (2020)
- [Making a Case for 3D Convolutions for Object Segmentation in Videos](https://mlsanity.com/paper/2008.11516/) (2020)
- [Enhancing Low-Cost Video Editing with Lightweight Adaptors and Temporal-Aware Inversion](https://mlsanity.com/paper/2501.04606/) (2025)
- [Generating Interpretable Poverty Maps using Object Detection in Satellite Images](https://mlsanity.com/paper/2002.01612/) (2020)
- [Understanding Nonlinear Collaboration between Human and AI Agents: A Co-design Framework for Creative Design](https://mlsanity.com/paper/2401.07312/) (2024)
- [Building-GAN: Graph-Conditioned Architectural Volumetric Design Generation](https://mlsanity.com/paper/2104.13316/) (2021)
- [Medical Image Segmentation with 3D Convolutional Neural Networks: A Survey](https://mlsanity.com/paper/2108.08467/) (2021)
- [Using Text-to-Image Generation for Architectural Design Ideation](https://mlsanity.com/paper/2304.10182/) (2023)
- [User Experience Design Professionals' Perceptions of Generative Artificial Intelligence](https://mlsanity.com/paper/2309.15237/) (2023)
- [Enhancing Intent Understanding for Ambiguous prompt: A Human-Machine Co-Adaption Strategy](https://mlsanity.com/paper/2501.15167/) (2025)
- [Graph2Plan: Learning Floorplan Generation from Layout Graphs](https://mlsanity.com/paper/2004.13204/) (2020)
- [Generalizing Alignment Paradigm of Text-to-Image Generation with Preferences through f-divergence Minimization](https://mlsanity.com/paper/2409.09774/) (2024)
- [FusionNet: 3D Object Classification Using Multiple Data Representations](https://mlsanity.com/paper/1607.05695/) (2016)
- [AI Art in Architecture](https://mlsanity.com/paper/2212.09399/) (2022)
- [TSCnet: A Text-driven Semantic-level Controllable Framework for Customized Low-Light Image Enhancement](https://mlsanity.com/paper/2503.08168/) (2025)
- [PromptLNet: Region-Adaptive Aesthetic Enhancement via Prompt Guidance in Low-Light Enhancement Net](https://mlsanity.com/paper/2503.08276/) (2025)
- [Deep Learning Architect: Classification for Architectural Design through the Eye of Artificial Intelligence](https://mlsanity.com/paper/1812.01714/) (2018)
- [Deep Learning for Detecting Building Defects Using Convolutional Neural Networks](https://mlsanity.com/paper/1908.04392/) (2019)
- [Ground-Fusion: A Low-cost Ground SLAM System Robust to Corner Cases](https://mlsanity.com/paper/2402.14308/) (2024)
- [Dissected 3D CNNs: Temporal Skip Connections for Efficient Online Video Processing](https://mlsanity.com/paper/2009.14639/) (2020)
- [From Heatmaps to Structural Explanations of Image Classifiers](https://mlsanity.com/paper/2109.06365/) (2021)
- [Structural Design Recommendations in the Early Design Phase using Machine Learning](https://mlsanity.com/paper/2107.08567/) (2021)

## Abstract (arXiv)

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.