I. Armeni, O. Sener, A. R. Zamir, H. Jiang, I. Brilakis et al., 3d semantic parsing of largescale indoor spaces, Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, 2016.

R. Blomley, B. Jutzi, and M. Weinmann, Classification of airborne laser scanning data using geometric multi-scale features and different neighbourhood types, ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, vol.3, issue.3 2, p.2016

A. Boulch, B. L. Saux, and N. Audebert, Unstructured point cloud semantic labeling using deep segmentation networks, Eurographics Workshop on 3D Object Retrieval, p.1, 2017.
DOI : 10.1016/j.cag.2017.11.010

N. Brodu and D. Lague, 3D terrestrial lidar data classification of complex natural scenes using a multi-scale dimensionality criterion: Applications in geomorphology, ISPRS Journal of Photogrammetry and Remote Sensing, vol.68, issue.2, pp.121-134, 2012.
DOI : 10.1016/j.isprsjprs.2012.01.006
URL : https://hal.archives-ouvertes.fr/insu-00700970

N. Chehata, L. Guo, and C. Mallet, Airborne lidar feature selection for urban classification using random forests. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, pp.38-46, 2009.

J. Demantke, C. Mallet, N. David, and B. Vallet, Dimensionality based scale selection in 3d lidar point clouds. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp.38-50, 2011.

A. Golovinskiy, V. G. Kim, and T. Funkhouser, Shape-based recognition of 3D point clouds in urban environments, 2009 IEEE 12th International Conference on Computer Vision, pp.2154-2161, 2009.
DOI : 10.1109/ICCV.2009.5459471

T. Hackel, N. Savinov, L. Ladicky, J. D. Wegner, K. Schindler et al., Semantic3d. net: A new Large-scale Point Cloud Classification Benchmark. arXiv preprint, 2017.

T. Hackel, J. D. Wegner, and K. Schindler, Fast semantic segmentation of 3d point clouds with strongly varying density . ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp.177-184, 2006.

J. Huang and S. You, Point cloud labeling using 3D Convolutional Neural Network, 2016 23rd International Conference on Pattern Recognition (ICPR), 2016.
DOI : 10.1109/ICPR.2016.7900038

A. E. Johnson and M. Hebert, Using spin images for efficient object recognition in cluttered 3D scenes, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.21, issue.5, pp.433-449, 1999.
DOI : 10.1109/34.765655

L. Landrieu and M. Simonovsky, Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs, 2007.
URL : https://hal.archives-ouvertes.fr/hal-01801186

F. J. Lawin, M. Danelljan, P. Tosteberg, G. Bhat, F. S. Khan et al., Deep Projective 3D Semantic Segmentation, International Conference on Computer Analysis of Images and Patterns, pp.95-107, 2017.
DOI : 10.1145/383259.383300
URL : http://arxiv.org/pdf/1705.03428

D. Munoz, N. Vandapel, and M. Hebert, Onboard contextual classification of 3-D point clouds with learned high-order Markov Random Fields, 2009 IEEE International Conference on Robotics and Automation, 2009.
DOI : 10.1109/ROBOT.2009.5152856

J. Niemeyer, F. Rottensteiner, and U. Soergel, Contextual classification of lidar data and building object detection in urban areas. ISPRS journal of photogrammetry and remote sensing, pp.152-165, 2014.

M. Pauly, R. Keiser, and M. Gross, Multi-scale Feature Extraction on Point-Sampled Surfaces, Computer graphics forum, pp.281-289, 2003.
DOI : 10.1145/566654.566584

C. R. Qi, H. Su, K. Mo, and L. J. Guibas, Pointnet: Deep learning on point sets for 3d classification and segmentation, Proc. Computer Vision and Pattern Recognition (CVPR), p.4, 2017.

C. R. Qi, L. Yi, H. Su, and L. J. Guibas, Pointnet++: Deep hierarchical feature learning on point sets in a metric space, Advances in Neural Information Processing Systems, pp.5099-5108, 2017.

X. Roynard, J. E. Deschaud, and F. Goulette, Fast and Robust Segmentation and Classification for Change Detection in Urban Point Clouds, ISPRS 2016-XXIII ISPRS Congress, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01355260

X. Roynard, J. Deschaud, and F. Goulette, Paris-Lille-3D: A large and high-quality ground-truth urban point cloud dataset for automatic segmentation and classification, The International Journal of Robotics Research, vol.34, issue.185, 2017.
DOI : 10.1016/j.isprsjprs.2015.01.016
URL : https://hal.archives-ouvertes.fr/hal-01695873

R. B. Rusu, N. Blodow, and M. Beetz, Fast Point Feature Histograms (FPFH) for 3D registration, 2009 IEEE International Conference on Robotics and Automation, pp.3212-3217, 2009.
DOI : 10.1109/ROBOT.2009.5152473

A. Serna and B. Marcotegui, Detection, segmentation and classification of 3D urban objects using mathematical morphology and supervised learning, ISPRS Journal of Photogrammetry and Remote Sensing, vol.93, issue.1, pp.243-255, 2014.
DOI : 10.1016/j.isprsjprs.2014.03.015
URL : https://hal.archives-ouvertes.fr/hal-01010012

A. Serna, B. Marcotegui, F. Goulette, and J. Deschaud, Paris-rue-Madame database: a 3d mobile laser scanner dataset for benchmarking urban detection, segmentation and classification methods, 4th International Conference on Pattern Recognition, Applications and Methods ICPRAM 2014, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00963812

R. Shapovalov, E. Velizhev, and O. Barinova, Nonassociative markov networks for 3d point cloud classification. the, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXVIII, Part 3A. Citeseer, 2010.

L. P. Tchapmi, C. B. Choy, I. Armeni, J. Gwak, and S. Savarese, SEGCloud: Semantic Segmentation of 3D Point Clouds, 2017 International Conference on 3D Vision (3DV), p.2017
DOI : 10.1109/3DV.2017.00067

B. Vallet, M. Brédif, A. Serna, B. Marcotegui, and N. Paparoditis, TerraMobilita/iQmulus urban point cloud analysis benchmark, Computers & Graphics, vol.49, issue.5, pp.126-133, 2015.
DOI : 10.1016/j.cag.2015.03.004
URL : https://hal.archives-ouvertes.fr/hal-01167995

M. Weinmann, B. Jutzi, S. Hinz, and C. Mallet, Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers, ISPRS Journal of Photogrammetry and Remote Sensing, vol.105, pp.286-304, 2005.
DOI : 10.1016/j.isprsjprs.2015.01.016

M. Weinmann, B. Jutzi, and C. Mallet, Feature relevance assessment for the semantic interpretation of 3D point cloud data, ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol.5, issue.2, p.2, 2013.
DOI : 10.5194/isprsannals-II-5-W2-313-2013