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Figure 1:3D point cloud with assigned labels (wire:blue,pole/trunk: red, fac¸ade: gray, ground: brown, vegetation: green).
Figure 2: The proposed framework: the contributions are high-lighted in red, and the quantity of attributes/approaches used forevaluation is indicated in green.
Table 1: Eigenvalue-based 3D features.
Table 8: Overall accuracy for RF-based classification using dif-ferent neighborhood definitions and different feature sets.

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