A. Kamal-aijazi, P. Checchin, and L. Trassoudaine, Segmentation Based Classification of 3D Urban Point Clouds: A Super-Voxel Based Approach with Evaluation, Remote Sensing, vol.63, issue.4, pp.1624-1650, 2013.
DOI : 10.1016/j.isprsjprs.2007.07.005

N. Sami-abu-el-haija, J. Kothari, P. Lee, G. Natsev, B. Toderici et al., Youtube-8m: A large-scale video classification benchmark. arXiv preprint, 2016.

[. Carlevaris-bianco, K. Arash, . Ushani, M. Ryan, and . Eustice, University of Michigan North Campus long-term vision and lidar dataset, The International Journal of Robotics Research, vol.35, issue.9, pp.1023-1035, 2016.
DOI : 10.1109/IROS.2012.6385561

M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler et al., The Cityscapes Dataset for Semantic Urban Scene Understanding, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.3213-3223, 2016.
DOI : 10.1109/CVPR.2016.350

J. Deng, W. Dong, R. Socher, L. Li, K. Li et al., ImageNet: A large-scale hierarchical image database, 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp.248-255, 2009.
DOI : 10.1109/CVPR.2009.5206848

F. Ferraro, N. Mostafazadeh, T. Huang, L. Vanderwende, J. Devlin et al., A survey of current datasets for vision and language research. arXiv preprint, 2015.

A. Geiger, P. Lenz, and R. Urtasun, Are we ready for autonomous driving? The KITTI vision benchmark suite, 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012.
DOI : 10.1109/CVPR.2012.6248074

. Goulette, . Nashashibi, . Abuhadrous, C. Ammoun, and . Laurgeau, An integrated on-board laser range sensing system for on-the-way city and road modelling. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, p.34, 2006.
URL : https://hal.archives-ouvertes.fr/hal-01259660

T. Hackel, D. Jan, K. Wegner, and . 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, 2016.

E. Andrew, M. Johnson, and . Hebert, Using spin images for efficient object recognition in cluttered 3d scenes. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.21, issue.5, pp.433-449, 1999.

M. Lin, S. Maire, J. Belongie, P. Hays, D. Perona et al., Microsoft COCO: Common Objects in Context, European Conference on Computer Vision, pp.740-755, 2014.
DOI : 10.1007/978-3-319-10602-1_48

[. Munoz, A. Bagnell, N. Vandapel, and M. Hebert, Contextual classification with functional Max-Margin Markov Networks, 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp.975-982, 2009.
DOI : 10.1109/CVPR.2009.5206590

D. Zoltan-csaba-marton, N. Pangercic, J. Blodow, M. Kleinehellefort, and . Beetz, General 3d modelling of novel objects from a single view, Intelligent Robots and Systems (IROS) IEEE/RSJ International Conference on, pp.3700-3705, 2010.

G. Maddern, C. Pascoe, P. Linegar, and . Newman, 1 year, 1000 km: The Oxford RobotCar dataset, The International Journal of Robotics Research, vol.2, issue.1, pp.3-15, 2017.
DOI : 10.1007/s11263-005-6644-8

D. Maturana and S. Scherer, VoxNet: A 3D Convolutional Neural Network for real-time object recognition, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp.922-928, 2015.
DOI : 10.1109/IROS.2015.7353481

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 Matching 3d models with shape distributions, Shape Modeling and Applications, SMI 2001 International Conference on, pp.152-165, 2001.

[. Pandey, R. James, . Mcbride, M. Ryan, and . Eustice, Ford Campus vision and lidar data set, The International Journal of Robotics Research, vol.30, issue.13, pp.1543-1552, 2011.
DOI : 10.1109/34.888718

URL : http://journals.sagepub.com/doi/pdf/10.1177/0278364911400640

R. Charles, H. Qi, K. Su, L. J. Mo, and . Guibas, Pointnet: Deep learning on point sets for 3d classification and segmentation. arXiv preprint, 2016.
DOI : 10.1109/cvpr.2017.16

URL : http://arxiv.org/pdf/1612.00593

G. Radu-bogdan-rusu, R. Bradski, J. Thibaux, and . Hsu, Fast 3d recognition and pose using the viewpoint feature histogram, Intelligent Robots and Systems (IROS) IEEE/RSJ International Conference on, pp.2155-2162, 2010.

[. Roynard, J. 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

D. Scharstein, H. Hirschmüller, Y. Kitajima, G. Krathwohl, N. Ne?i´cne?i´c et al., High-Resolution Stereo Datasets with Subpixel-Accurate Ground Truth, German Conference on Pattern Recognition, pp.31-42, 2014.
DOI : 10.1007/978-3-319-11752-2_3

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, 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

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

URL : https://hal.archives-ouvertes.fr/hal-01167995

A. Velizhev, R. Shapovalov, and K. Schindler, IMPLICIT SHAPE MODELS FOR OBJECT DETECTION IN 3D POINT CLOUDS, International Society of Photogrammetry and Remote Sensing Congress, 2012.
DOI : 10.5194/isprsannals-I-3-179-2012

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, 2015.
DOI : 10.1016/j.isprsjprs.2015.01.016