Outputs
3D reconstruction of olive trees — from real-world point clouds to interactive, prunable models in Unreal Engine.
As part of the OliVR pipeline, real olive trees are captured as 3D point clouds, reconstructed into clean meshes, and brought into a real-time engine where pruning can be performed interactively — forming the basis of the project’s VR training modules.

For each branch segment of the tree, the following attributes were extracted: diameter, length, angle from vertical, height, topological depth, subtree size, number of children, centroid coordinates (x, y, z) and leaf count, together with a one-hot encoding of the branch class; all attributes were normalised using z-scores.
A separate CSV file was generated for each tree, containing, for every branch segment, its coordinates, whether it should be pruned, the pruning rationale, the pruning tool to be used, the cut type and the coordinates of the cut point.
These CSV files were produced by a rule-based decision model, and the GNN was subsequently trained on the pruning labels contained in them; the CSV is therefore the input to the GNN analysis rather than its output.
A Graph Neural Network (GNN) analysis was then performed on the resulting graph, in which spatial nearest-neighbour edges were added alongside the botanical parent–child relationships between nodes. This approach made it possible to partially recover neighbourhood-context information that is not contained in a branch's own geometry — such as "the branch is surrounded by dense branching" and "the branch is overshadowed" — whereas no such gain was observed when the botanical tree graph was used alone.
VARIETY 01 - ARBEQUINA
Arbequina Olive Tree

VARIETY 02 - DOMAT
Domat Olive Tree

VARIETY 03 - KORONEIKI
Koroneiki Olive Tree

INTERACTIVE - UNREAL ENGINE
Branch Pruning in Unreal Engine


VARIETY 04 - MEMECİK
Memecik Olive Tree

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