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    PlanNet, a novel generative model, synthesizes component-based floor plans using a wave function collapse algorithm and deep neural networks. It generates a large dataset and enables interactive design for complex 3D scenes.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Computational Geometry

    Background:

    • Floor plan generation is crucial for architectural design and virtual environments.
    • Existing methods often lack flexibility and interactivity in creating complex, component-based layouts.

    Purpose of the Study:

    • To introduce PlanNet, a novel generative model for component-based plan synthesis.
    • To enable interactive design and generation of large-scale, complex floor plans and 3D scenes.

    Main Methods:

    • Utilizes a wave function collapse algorithm for initial wireframe patterns.
    • Employs two deep neural networks for boundary outlining and semantic labeling.
    • Generates a dataset of 10,000 component-based plans.
    • Incorporates an interactive workflow for plan subdivision and refinement.
    • Uses a random selection algorithm for semantic label variation.

    Main Results:

    • Successfully generated a large-scale component-based plan dataset (10K examples).
    • Demonstrated effective plan synthesis with hard and soft boundary constraints.
    • Enabled the creation of complex 3D scenes through interactive subdivision and semantic variation.
    • Achieved superior performance compared to state-of-the-art floor plan synthesis methods.

    Conclusions:

    • PlanNet offers a feasible and effective approach to generative floor plan synthesis.
    • The interactive workflow empowers users to design complex and customized architectural layouts.
    • The model's versatility supports the creation of diverse 3D scenes.