LoD Sketch Extraction from Architectural Models Using Generative AI: Dataset Construction for Multi-Level Architectural Design Generation

2026-01-23

AI Summary

This study addresses the inefficiency and geometric inconsistency of traditional manual multi-level-of-detail (LoD) building modeling, as well as the scarcity of high-quality paired data that hinders generative AI applications in this domain. To overcome these limitations, the authors propose the first end-to-end generative AI framework that integrates computer vision and generative modeling to achieve progressive, semantically controllable simplification from high-detail models (e.g., LoD3) to abstract block representations (e.g., LoD1), ensuring both geometric consistency and hierarchical coherence. The method achieves SSIM scores of 0.7319 and 0.7532 for LoD3→LoD2 and LoD2→LoD1 transitions, respectively, with normalized Hausdorff distances of 25.1% and 61.0% relative to the image diagonal. Furthermore, the work introduces the first high-quality paired multi-LoD dataset, advancing AI-driven architectural generation research.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

For architectural design, representation across multiple Levels of Details (LoD) is essential for achieving a smooth transition from conceptual massing to detailed modeling. However, traditional LoD modeling processes rely on manual operations that are time-consuming, labor-intensive, and prone to geometric inconsistencies. While the rapid advancement of generative artificial intelligence (AI) has opened new possibilities for generating multi-level architectural models from sketch inputs, its application remains limited by the lack of high-quality paired LoD training data. To address this issue, we propose an automatic LoD sketch extraction framework using generative AI models, which progressively simplifies high-detail architectural models to automatically generate geometrically consistent and hierarchically coherent multi-LoD representations. The proposed framework integrates computer vision techniques with generative AI methods to establish a progressive extraction pipeline that transitions from detailed representations to volumetric abstractions. Experimental results demonstrate that the method maintains strong geometric consistency across LoD levels, achieving SSIM values of 0.7319 and 0.7532 for the transitions from LoD3 to LoD2 and from LoD2 to LoD1, respectively, with corresponding normalized Hausdorff distances of 25.1% and 61.0% of the image diagonal, reflecting controlled geometric deviation during abstraction. These results verify that the proposed framework effectively preserves global structure while achieving progressive semantic simplification across different LoD levels, providing reliable data and technical support for AI-driven multi-level architectural generation and hierarchical modeling.

Problem

• Levels of Detail
• architectural modeling
• generative AI
• training data
• sketch extraction

Innovation

• Generative AI
• Level of Detail (LoD)
• Architectural Sketch Extraction
• Hierarchical Modeling
• Geometric Consistency