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Figure 1. Illustration of the different steps of our method: the pointwise, irregular classification 1b is combined with the geometricallyhomogeneous segmentation 1c to obtain a smooth, objects-aware classification 1d
Figure 2. Means and standard deviations of the local descriptorsin the Oakland dataset for the following classes: wires, poles,façades, roads, vegetation.
Figure 5. Adjacency structure of the segment-graph. The edgesbetween points are represented in black, the segmentationand the adjacency of its components in blue:.
Table 2. Precision, recall and FScore in % for the Semantic3D benchmark. The global accuracy are respectively 88.4%, 96.9% et97.2%

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