A Dual-Aspect Evaluation Framework for Architectural Plan Generation Using pix2pix-Series Algorithms
Architectural plan generation using pix2pix-series algorithms presents significant challenges, particularly regarding the absence of domain-specific evaluation standards and limited understanding of how training configurations jointly influence performance. Current architectural AI workflows focus heavily on visual output, often overlooking principle adherence, vectorization quality, and the structural logic of predicted plans. Addressing this gap, the proposed framework introduces a systematic experimental design and a dual-aspect evaluation approach specifically tailored for architectural-like plans, establishing a rigorous foundation for AI-enabled generative design in architecture.
Limitations of Existing pix2pix Adaptations in Architectural Design
Although pix2pix architectures are widely adopted for map translation and image-to-image tasks, their adaptation to architectural plan generation remains underdeveloped due to the lack of domain-centered evaluation benchmarks. Architectural drawings demand geometric precision, consistent line representation, and semantic correctness—requirements unmet by generic similarity metrics alone. Existing studies also fail to explore how dataset scale, annotation richness, and algorithmic variations jointly affect model learning. These limitations highlight the need for a refined, architecture-oriented experimental framework.
Synthetic Dataset Construction for Architectural-Like Plan Generation
To support comprehensive experimentation, we built a high-quality, large-volume synthetic dataset featuring diverse architectural-like plans with consistent structural logic and enhanced annotation fidelity. The dataset captures variations in resolution, complexity, and line density, enabling controlled analysis of model behavior across different architectural contexts. This synthetic approach ensures scalability, uniformity, and precision—critical for training and evaluating generative models intended for professional architectural workflows and early-stage design automation.
Multi-Configuration Experimental Design and Model Generation
Our research involved 12 systematic experiments combining variations in training set sizes, dataset features, and pix2pix-series algorithms. Each experiment generated intermediate checkpoints, resulting in 240 trained generative models evaluated on a constant test set. This multi-configuration design enables granular insights into how training data scale, annotation richness, and algorithm type interact to influence generative performance. The methodology offers a replicable and domain-adapted pipeline for future architectural AI studies.
Dual-Aspect Evaluation: Pixel Similarity and Segmentation Line Continuity
To address the lack of architectural-specific assessment tools, we developed a dual-aspect evaluation method that measures both principle adherence and vectorization quality. Pixel similarity quantifies how closely predictions follow predefined architectural rules and spatial patterns, while segmentation line continuity evaluates the geometric clarity and connectivity essential for downstream vector-based applications. This combined evaluation captures both visual fidelity and structural logic, offering a robust benchmark for architectural plan generation models.
Findings, Optimal Model Performance, and Practical Implications
Results identified algorithm type and training set size as dominant performance factors, with larger datasets amplifying the benefits of high-resolution and detailed-annotation inputs. The best-performing model achieved strong principle adherence (0.81 similarity) and excellent vectorization potential (0.86 segmentation continuity), proving capable of producing reliable, structure-aware predictions. Validation on 7,695 test samples confirmed its robustness and controlled creativity, further supported through successful 3D model conversion. These outcomes establish a practical bridge between generative research and real-world architectural design applications.
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