Tree Reconstruction from a Point Cloud using an L-system
D.J. Dobson (TU Delft - Architecture and the Built Environment)
H. Dong (TU Delft - Architecture and the Built Environment)
N. van der Horst (TU Delft - Architecture and the Built Environment)
L.M. Langhorst (TU Delft - Architecture and the Built Environment)
J.A.J. van der Vaart (TU Delft - Architecture and the Built Environment)
Z. Wu (TU Delft - Architecture and the Built Environment)
L. Nan – Mentor (TU Delft - Urban Data Science)
S. Du – Mentor (TU Delft - Urban Data Science)
Dirk Voets – Mentor (Cobra Groeninzicht)
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Abstract
Storing accurate models of complex geometries in a compact way has become an increasingly challenging issue, especially when dealing with large datasets. One of such datasets is Cobra-Groeninzicht's database of all trees in the Netherlands. In the gaming industry, a new technique is being used to generate tree models: the L-system. An L-system stores a string representation of the structural model of a tree, with the added possibility for recursive modelling using growing rules. This format proves a promising alternative to more traditional methods of storing complex geometries. However, it remains unclear whether it can be an accurate enough representation for modelling and analysing real-life trees.
In this research project, the AdTree algorithm is used to reconstruct a skeleton from a point cloud of a single tree. This skeleton is then transformed to an L-System string format, as well as a CityJSON format (both in JSON structure). The L-system format comes with the advantage that it allows for several methods of increasing its compactness further (growing, generalisation). The overall size of these files also indicates fewer storage space is needed to store the tree geometry. The quality of the L-System skeleton is nearly equal to the input, the skeleton generated by. Assuming it can be read and drawn using a Turtle program, the L-system thus allows for storing the same geometric information more compactly than traditional storage formats, with sufficient accuracy, and the added possibilities of growing or generalising the model.