Layout2Rendering: AI-aided Greenspace design
<|reference_start|>Layout2Rendering: AI-aided Greenspace design: In traditional human living environment landscape design, the establishment of three-dimensional models is an essential step for designers to intuitively present the spatial relationships of design elements, as well as a foundation for conducting landscape analysis on the site. Rapidly and effectively generating beautiful and realistic landscape spaces is a significant challenge faced by designers. Although generative design has been widely applied in related fields, they mostly generate three-dimensional models through the restriction of indicator parameters. However, the elements of landscape design are complex and have unique requirements, making it difficult to generate designs from the perspective of indicator limitations. To address these issues, this study proposes a park space generative design system based on deep learning technology. This system generates design plans based on the topological relationships of landscape elements, then vectorizes the plan element information, and uses Grasshopper to generate three-dimensional models while synchronously fine-tuning parameters, rapidly completing the entire process from basic site conditions to model effect analysis. Experimental results show that: (1) the system, with the aid of AI-assisted technology, can rapidly generate space green space schemes that meet the designer's perspective based on site conditions; (2) this study has vectorized and three-dimensionalized various types of landscape design elements based on semantic information; (3) the analysis and visualization module constructed in this study can perform landscape analysis on the generated three-dimensional models and produce node effect diagrams, allowing users to modify the design in real time based on the effects, thus enhancing the system's interactivity.<|reference_end|>
@article{chen2024layout2rendering:,
    title={Layout2Rendering: AI-aided Greenspace design},
    author={Ran Chen, Zeke Lian, Yueheng He, Xiao Ling, Fuyu Yang, Xueqi Yao,
  Xingjian Yi, Jing Zhao},
    journal={arXiv preprint arXiv:2404.16067},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.16067},
    primaryClass={cs.HC cs.AI}
}