Layout2Rendering: AI-aided Greenspace design
This addresses the problem of slow, parameter-limited 3D model generation in landscape design for designers, though it appears incremental as it combines existing deep learning with tools like Grasshopper.
This study tackles the challenge of rapidly generating realistic 3D landscape designs by proposing a deep learning-based system that creates park space plans from topological relationships, vectorizes elements, and uses Grasshopper for 3D modeling with real-time parameter tuning. The system can quickly generate green space schemes from site conditions, vectorize and 3D-ize landscape elements semantically, and provide interactive analysis and visualization for real-time design modifications.
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.