Layout2Rendering: AI-aided Greenspace design
Ran Chen, Zeke Lian, Yueheng He, Xiao Ling, Fuyu Yang, Xueqi Yao,, Xingjian Yi, Jing Zhao

TL;DR
This paper introduces an AI-assisted system for rapid, realistic landscape design that generates 3D models based on landscape element relationships, enabling real-time analysis and interactive modifications.
Contribution
The study presents a novel deep learning-based system that generates landscape designs from topological relationships and integrates real-time analysis and editing capabilities.
Findings
Rapid generation of landscape schemes meeting designer perspectives
Vectorization and 3D modeling of landscape elements based on semantics
Interactive landscape analysis and real-time design modifications
Abstract
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…
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Taxonomy
Topics3D Modeling in Geospatial Applications
