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, Common workflow of model-driven approaches, p.73
, 2 Common workflow of projection detection approaches, p.74
, Common workflow of 3D mathematical model approximation approaches
, Common workflow of 3D complex and free shape detection approaches
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, 115 10.2 Semantic description of the data domain
, 118 10.5 Illustration of the influence of the object material on the acquisition (asphalt sidewalk in the lower part of the image and a metal traffic sign in the upper part of the image)
, Illustration of two walls from two different scene contexts: a) wall in ruin excavation context, b) wall in modern building context, p.121
, Semantic description of any algorithm
, 128 11.2 Illustration of the normal estimation. The value of the normal is converted in RGB format for the visualisation, p.129
, 7 Example of a point cloud containing cars that have been detected (yellow) and not detected (blue) before the learning process, p.141
, 150 13.1 The knowledge data processing modeling for the region growing algorithm
, 153 13.3 Density computation results based on the knowledge of the sensing process: high density in red and yellow, medium density in green, and low density in blue
, Different walls representation in the study cases considered, p.161
, Illustration of topological hierarchy between objects, p.163
, Algorithms graph for the floor detection
, Results of the sampling algorithm for the three application cases concerned
, Normal Filtering" algorithm for the floor detection in application cases studied
, Normal Region Growing" algorithm for the floor detection in the application cases studied. A single color is assigned to each segment
, Results of floors detection on application cases studied, p.172
, Normal Filtering" algorithm for walls detection in application cases studied
, Normal Region Growing" algorithm for walls detection in the application cases studied. A single color is assigned to each segment
, 178 14.11Descriptions of the watermill for the application case 2.1.2 (a) Point cloud with watermill (room illustrated in yellow); (b) Floor plot of the watermill; (c) Schematic geometric descriptions; (d) Schematic knowledge representation, 14.10Results of the rooms classification
, 186 14.18Comparison of cars detection results without and with the selflearning process on Paris-Rue Madame dataset, p.186
, Results on the application case 2.1.4: each object classified as belonging to the same type are represented by the same color, elements colored in black and red are unclassified elements, p.190
, Illustration of round edge in red of the table, p.191
, 195 15.5 a) Illustration of rooms parts not classified in red and a wrong classified in purple, b) Zoom on the representation of these three room parts
, Ground truth of the point cloud used as reference
, 15.8 Results on the application case 2.1.3 from "Paris-rue-Madame database: MINES ParisTech 3D mobile laser scanner dataset from Madame street in Paris"
, 204 15.11Illustration of the results comparison, the black color represents rooms wrongly detected; other colors represent rooms well detected: a) the ground truth, b) the reconstruction results of, p.206, 2014.
, A.2 Illustration of the volume choice in red for a sidewalk, p.262
, Illustration of the sampling process (point clouds are colored for better viewing)
, Normals are colored in red, green, and blue, according to their X,Y,Z values respectively
, Illustration of the filtering of a point cloud
, Results of a segmentation by a region growing algorithm, p.266
, Organizational chart of the RANSAC algorithm, p.268
, Results of detection of a unique sphere by the RANSAC algorithm, p.269
, Results of detection of two planes by the RANSAC algorithm, p.270
, 2 Conversion table of OWL-restriction to Manchester syntax, p.64
, , p.149
, 203 15.5 Comparison of metrics obtained before and after Self-Learning process (S-L) on Paris-Rue-Madame dataset, p.206
, 207 15.8 Results of metric values obtained by each approach for each considered object class: the best score by metric and object is highlighted by a green background