. Aijazi, 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

URL : https://hal.archives-ouvertes.fr/hal-01655574

. Armeni, 3D Semantic Parsing of Large-Scale Indoor Spaces, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
DOI : 10.1109/CVPR.2016.170

. Boulch, 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

. Brock, Generative and discriminative voxel modeling with convolutional neural networks, 2016.

. Deng, 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

. Engelmann, Exploring Spatial Context for 3D Semantic Segmentation of Point Clouds, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp.716-724, 2017.
DOI : 10.1109/ICCVW.2017.90

. Hackel, Seman- tic3d.net: A new large-scale point cloud cassification benchmark, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp.1-1, 2017.

. Hackel, 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.

. Hu, Squeeze-and-Excitation Networks, 2017.

J. Huang and S. You, Point cloud labeling using 3D Convolutional Neural Network, 2016 23rd International Conference on Pattern Recognition (ICPR), pp.2670-2675, 2016.
DOI : 10.1109/ICPR.2016.7900038

. Kingma, . Ba, D. P. Kingma, and J. Ba, Adam: A method for stochastic optimization. arXiv preprint, 2014.

. Landrieu, A structured regularization framework for spatially smoothing semantic labelings of 3D point clouds, ISPRS Journal of Photogrammetry and Remote Sensing, vol.132, pp.102-118, 2017.
DOI : 10.1016/j.isprsjprs.2017.08.010

URL : https://hal.archives-ouvertes.fr/hal-01505245

. Landrieu, . Simonovsky, L. Landrieu, and M. Simonovsky, Large-scale point cloud semantic segmentation with superpoint graphs. arXiv preprint, 2017.

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

. Qi, Pointnet: Deep learning on point sets for 3d classification and segmentation. arXiv preprint, 2016.

. Qi, Pointnet++: Deep hierarchical feature learning on point sets in a metric space, Advances in Neural Information Processing Systems, pp.5105-5114, 2017.

. Roynard, Fast and robust segmentation and classification for change detection in urban point clouds, ISPRS -International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp.3693-699, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01355260

. Roynard, Paris-Lille-3D: a large and high-quality ground truth urban point cloud dataset for automatic segmentation and classification, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01695873

M. Serna, 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

. Sfikas, Ensemble of PANORAMA-based convolutional neural networks for 3D model classification and retrieval, Computers & Graphics, vol.71, 2017.
DOI : 10.1016/j.cag.2017.12.001

Z. Simonyan, K. Simonyan, and A. Zisserman, Very Deep Convolutional Networks for Large- Scale Image Recognition, 2014.

. Szegedy, Inception-v4, inception-resnet and the impact of residual connections on learning, AAAI, p.12, 2017.

. Tchapmi, Segcloud: Semantic segmentation of 3d point clouds. arXiv preprint, 2017.

. Weinmann, 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

. Wu, 3d shapenets: A deep representation for volumetric shapes, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.1912-1920, 2015.

. Yi, Large-scale 3d shape reconstruction and segmentation from shapenet core55, 2017.

. Zhang, Sensor fusion for semantic segmentation of urban scenes, 2015 IEEE International Conference on Robotics and Automation (ICRA), pp.1850-1857, 2015.
DOI : 10.1109/ICRA.2015.7139439