Convenient Tree Species Modeling for Virtual Cities

Abstract

Generating large scale 3D tree models for digital twin cities at a species level-of-detail poses challenges of automation and maintenance of such dynamically evolving models. This paper presents an inverse procedural modeling methodology to automate the generation of 3D tree species models based on growth spaces from point clouds and pre-formulated L-system growth rules. The rules capture the botanical tree architecture at a species level in terms of growth process, branching pattern, and responses to external stimuli. Users only need to fill in a species profile template and provide the growth space derivable from the point clouds. The parameters involved in the rules are automatically optimised within the growth space to produce the species models to represent actual trees. This methodology enables users without 3D modeling skills to conveniently produce highly representative 3D models of any tree species in a large scale.