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
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
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
Generative and discriminative voxel modeling with convolutional neural networks, 2016. ,
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
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
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. ,
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. ,
Squeeze-and-Excitation Networks, 2017. ,
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
Adam: A method for stochastic optimization. arXiv preprint, 2014. ,
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
Large-scale point cloud semantic segmentation with superpoint graphs. arXiv preprint, 2017. ,
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
Pointnet: Deep learning on point sets for 3d classification and segmentation. arXiv preprint, 2016. ,
Pointnet++: Deep hierarchical feature learning on point sets in a metric space, Advances in Neural Information Processing Systems, pp.5105-5114, 2017. ,
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
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
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
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
Very Deep Convolutional Networks for Large- Scale Image Recognition, 2014. ,
Inception-v4, inception-resnet and the impact of residual connections on learning, AAAI, p.12, 2017. ,
Segcloud: Semantic segmentation of 3d point clouds. arXiv preprint, 2017. ,
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
3d shapenets: A deep representation for volumetric shapes, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.1912-1920, 2015. ,
Large-scale 3d shape reconstruction and segmentation from shapenet core55, 2017. ,
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