Generating large scale 3D tree models for digital twin cities at a species level-of-detail poses challenges of automation and main- tenance 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, branch- ing 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 automati- cally 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.
License type:
http://creativecommons.org/licenses/by-nc-nd/4.0/
Funding Info:
This work is supported by National Research Foundation Singapore, Virtual Singapore Award no. NRF2015VSG-AA3DCM001-034. Authors thank colleagues at IHPC (A*STAR), NParks, and GovTech for their valuable input and support.
Description:
This is a post-peer-review, pre-copyedit version of an article published in Advances in Computer Graphics. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-030-22514-8_25