Computer-aided layout generation for building design: A review

Generating realistic building layouts for automatic building design has been studied in both computer vision and architectural domains. Traditional approaches in the latter, which are based on optimization techniques or heuristic design guidelines, can synthesize desirable layouts, but usually require post-processing and involve human interaction in the design pipeline, making them costly and time-consuming. The advent of deep generative models has significantly improved the fidelity and diversity of the generated architecture layouts, reducing the workload of designers and making the process much more efficient. This paper presents a comprehensive review of three major research topics in architectural layout design and generation: floorplan layout generation, scene layout synthesis, and generation of various other formats of building layouts. For each topic, we overview the leading paradigms, categorized either by research domains (architecture or machine learning) or by user input conditions or constraints. We then introduce commonly-adopted benchmark datasets used to verify the effectiveness of the methods, as well as corresponding evaluation metrics. Finally, we identify the well-solved problems and limitations of existing approaches, and then propose promising directions for future research. This survey has an associated project which aims to maintain the resources, at https://github.com/jcliu0428/awesome-building-layout-generation.