TreeSpecies-PC2DT: Automated Tree Species Modeling from Point Clouds to Digital Twins

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TreeSpecies-PC2DT: Automated Tree Species Modeling from Point Clouds to Digital Twins
Title:
TreeSpecies-PC2DT: Automated Tree Species Modeling from Point Clouds to Digital Twins
Journal Title:
Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
Keywords:
Publication Date:
03 March 2024
Citation:
Gobeawan, L., Liu, X., Lim, C., Raghavan, V., Chattoraj, J., Schindler, J., & Yang, F. (2024). TreeSpecies-PC2DT: Automated Tree Species Modeling from Point Clouds to Digital Twins. Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. https://doi.org/10.5220/0012389700003660
Abstract:
3D digital twin trees for a city-scale have been limited to low-resolution, static shape models due to challenges in automation/scalability, cost performance, tree growth dynamics, species complexities and compatibilities with simulations and virtual city platforms. To address those challenges for high-resolution tree models, we propose an automated workflow of generating large-scale, lightweight, dynamic digital-twin tree species models from point cloud data. Species digital twins are modelled as detailed hierarchical branch structures by solving for all species profile parameters through stages of branch reconstruction from point cloud data, species profiling by machine learning, tropism transfer, optimisation and species growth modelling based on botany and limited field survey. We show that the generated high-resolution tree models can be lightweight while representing their true species characteristics and dynamic botanical architecture (branching patterns and growth processes).
License type:
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Funding Info:
This research / project is supported by the National Research Foundation, Singapore - Joint New Zealand - Singapore Data Science Research Programme
Grant Reference no. : SDSC-2020-002
Description:
ISBN:
978-989-758-679-8