A Deep Learning-Based Framework for Converting Architectural Sketches into Structured 3D Models
Deep Learning Integration in Architectural Design
The research demonstrates how deep learning tools such as Stable Diffusion, CycleGAN, and Pixel2Mesh can be strategically aligned with architectural reasoning. These models support essential phases of conceptual development—ranging from exploring alternatives to extracting depth and generating formal massing. The framework leverages AI not merely as an automation tool but as an intelligent collaborator that interprets sketch inputs and transforms them into structured design outcomes. This cross-disciplinary integration enhances the creative and analytical capacity of architects and researchers.
Addressing the 2D-to-3D Domain Gap
A core challenge in computational architecture is the mismatch between 2D sketches and accurate 3D model generation. This study resolves the domain gap by embedding domain-specific knowledge and iterative learning cycles into the process. By combining image retrieval methods, generative models, and mesh reconstruction systems, the framework ensures that both conceptual intent and structural integrity are preserved during transformation. The result is a more reliable and context-aware 3D generation pipeline suited to architectural applications.
Phased and Iterative Design Workflow
Reflecting the natural logic of architectural design, the framework is structured into two phases: 2D design and 3D design. The phased workflow ensures clarity and control, while iterative loops allow designers to refine output at each stage. The model supports conceptual exploration through multi-scheme generation and advances toward detailed form generation with parametric optimization. This cyclical system mirrors real-world design methodology, enhancing both the practicality and authenticity of AI-assisted modeling.
Performance Validation and Architectural Accuracy
The study uses structural similarity, geometric accuracy, and qualitative fidelity assessments to evaluate the generated models. Results reveal that the deep learning-based system maintains architectural correctness while enabling creative variation—an essential balance in computational design. The ability to generate accurate forms without losing conceptual fluidity marks a significant advancement in automated architectural modeling.
Case Study and Research Implications
A Mars habitat design case study demonstrates the adaptability and scientific value of the framework in academic research settings. While the system proves effective in controlled experiments, the study acknowledges the need for broader validation across more complex typologies. This work bridges the gap between traditional design logic and emerging AI methodologies, contributing to a future where architectural innovation is amplified by intelligent, cross-disciplinary systems.
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