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

TL;DR

This paper introduces an AI-based framework for automatically extracting multi-level LoD sketches from architectural models, facilitating efficient and consistent hierarchical modeling for architectural design.

Contribution

It presents a novel automatic LoD sketch extraction method combining computer vision and generative AI, addressing the lack of high-quality training data for multi-level architectural modeling.

Findings

Achieves high geometric consistency with SSIM > 0.73 across LoD transitions.

Maintains global structure while simplifying models progressively.

Provides reliable data for AI-driven multi-level architectural generation.

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.