Optimized urban vegetation layouts balance shade and aesthetics
Greenscapes: Optimized Vegetation Configurations in Cities
GraphicsComputer Vision and Pattern Recognition
Summary
Vegetation in cities helps with shade, sound control, climate, and makes places look nice. The authors created a new way to decide where to plant trees and shrubs in 3D city views. They use a tool that understands pictures and language to suggest plant positions, then improve these placements with a computer method aiming for better shade and noise control. This method also considers how the scene looks from different views to keep it attractive. It helps design city green spaces that are both useful and good to see.
What this means in practice
- •For urban landscape designers: Generate 3D plant layouts that optimize shade, noise reduction, and aesthetic appeal for city lots automatically.
- •For game developers: Create realistic and optimized city green spaces by placing vegetation automatically in 3D urban scenes.$Commercial implications: Enables advanced city environment generation tools that enhance realism and environmental effects for games.
Authors
Patryk Bartkowiak, Marcin Marlewski, Paweł Wójcik, Jae Joong Lee, Dominik L. Michels, Bedrich Benes, Sören Pirk, Wojciech Pałubicki
Abstract
Vegetation plays a central role in cities, serving functional roles such as balancing shade and sky exposure, shaping the microclimate, carbon sequestration, and providing acoustic screening. Furthermore, aesthetic choices in landscape styles (formal, modernist, picturesque) are important for citizens' well-being and should be considered in the design of urban landscapes. In this paper, we present a novel method for placing vegetation, ranging from shrubs to large trees, in 3D urban scenes. Our method uses individual lots as input and employs a conditioned Vision-Language Model (VLM) to generate a top-down rendering of vegetation, placing large trees, small trees, and shrubs on lawn areas. This rendering is then converted into a 3D scene by detecting individual plants and selecting appropriate 3D assets. To refine the proposed configurations, we define an optimization loop that keeps the VLM-generated positions fixed after a deterministic feasibility repair and uses CMA-ES to optimize per-instance scale transformations along with discrete vegetation asset substitutions. To define the objective function for the optimization, we evaluate the generated 3D scenes with efficient surrogate models of shade and path-based acoustic exposure. Furthermore, we render in-scene views at each optimization step to compute view-dependent perceptual descriptors, enabling the generation of both aesthetic and functional vegetation layouts for urban scenes.