WallPlan: synthesizing floorplans by learning to generate wall graphs

Abstract

Floorplan generation has drawn widespread interest in the community. Recent learning-based methods for generating realistic floorplans have made significant progress while a complex heuristic post-processing is still necessary to obtain desired results. In this paper, we propose a novel wall-oriented method, called WallPlan, for automatically and efficiently generating plausible floorplans from various design constraints. We pioneer the representation of the floorplan as a wall graph with room labels and consider the floorplan generation as a graph generation. Given the boundary as input, we first initialize the boundary with windows predicted by WinNet. Then a graph generation network GraphNet and semantics prediction network LabelNet are coupled to generate the wall graph progressively by imitating graph traversal. WallPlan can be applied for practical architectural designs, especially the wall-based constraints. We conduct ablation experiments, qualitative evaluations, quantitative comparisons, and perceptual studies to evaluate our method's feasibility, efficacy, and versatility. Intensive experiments demonstrate our method requires no post-processing, producing higher quality floorplans than state-of-the-art techniques.

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Index Terms

• Computing methodologies

  • Artificial intelligence

    • Knowledge representation and reasoning

      • Probabilistic reasoning

  • Computer graphics

    • Shape modeling

  • Machine learning

    • Machine learning approaches

      • Neural networks