Towards the digital twin of urban forest: 3D modeling and parameterization of large-scale urban trees from close-range laser scanning
Chen Chen, Haodong Wang, Duanchu Wang, Duanchu Wang, Di Wang, Di Wang
Abstract
Trees play a crucial role in urban environment, offering distinct ecological and aesthetic values. Fine-grained urban tree models hold valuable potential for urban landscape planning and green space management. Consequently, in recent years, how to reconstruct detailed tree models using digital twin technology has become a focal point of interest. Point cloud data has become a major source for tree modeling because of its unique capability to represent objects’ geometry. However, current methods for tree reconstruction primarily concentrate on individual trees, and necessitate high-quality point cloud data that is arduous to acquire on a large scale within complex urban settings. Furthermore, adequately storing and managing tree models of a large scale is another vital challenge to address. In response to these challenges, we propose a novel approach for 3D modeling and parameterization of large-scale urban trees based on point clouds acquired from consumer-grade mobile laser scanning (MLS) and UAV laser scanning (ULS) platforms. Our pipeline encompasses several key techniques: tree extraction, adaptive tree modeling, and model parameterization. To validate our approach, we collected MLS and ULS data covering an area of 36,400 m2. Using the proposed pipeline, we achieved large-scale tree modeling and lightweight model representation with a success rate of 96%. Both qualitative and quantitative evaluations have proved the effectiveness of our reconstruction method in terms of visual quality and estimation of tree structure parameters. The generated tree models, amenable to lightweight representation, facilitate integration and contribute to the advancement of digital urban forest construction, management, and applications.