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In this paper, a labelling method for the semantic analysis of ultra-high point density MLS data (up to 4000 points/m<sup>2</sup>) in urban road\udcorridors is developed based on combining a conditional random field (CRF) for the context-based classification of 3D point clouds\udwith shape priors. The CRF uses a Random Forest (RF) for generating the unary potentials of nodes and a variant of the contrastsensitive\udPotts model for the pair-wise potentials of node edges. The foundations of the classification are various geometric features\udderived by means of co-variance matrices and local accumulation map of spatial coordinates based on local neighbourhoods.\udMeanwhile, in order to cope with the ultra-high point density, a plane-based region growing method combined with a rule-based\udclassifier is applied to first fix semantic labels for man-made objects. Once such kind of points that usually account for majority of\udentire data amount are pre-labeled; the CRF classifier can be solved by optimizing the discriminative probability for nodes within a\udsubgraph structure excluded from pre-labeled nodes. The process can be viewed as an evidence fusion step inferring a degree of belief\udfor point labelling from different sources. The MLS data used for this study were acquired by vehicle-borne Z+F phase-based laser\udscanner measurement, which permits the generation of a point cloud with an ultra-high sampling rate and accuracy. The test sites are\udparts of Munich City which is assumed to consist of seven object classes including impervious surfaces, tree, building roof/facade, low\udvegetation, vehicle and pole. The competitive classification performance can be explained by the diverse factors: e.g. the above ground\udheight highlights the vertical dimension of houses, trees even cars, but also attributed to decision-level fusion of graph-based contextual\udclassification approach with shape priors. The use of context-based classification methods mainly contributed to smoothing of labelling\udby removing outliers and the improvement in underrepresented object classes. In addition, the routine operation of a context-based\udclassification for such high density MLS data becomes much more efficient being comparable to non-contextual classification schemes

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

SEMANTIC LABELLING OF ULTRA DENSE MLS POINT CLOUDS IN URBAN ROAD CORRIDORS BASED ON FUSING CRF WITH SHAPE PRIORS