A. Broggi, A. Zelinsky, U. Ozguner, and C. Laugier, Handbook of Robotics 2nd edition, Chapter 62 on "Intelligent Vehicles, Handbook of Robotics 2nd Edition, pp.1627-1656, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01260280

Y. Oh and Y. Watanabe, Development of small robot for home floor cleaning, SICE 2002. Proceedings of the 41st SICE Annual Conference, vol.5, p.1, 2002.

Y. Park, V. Lepetit, and W. Woo, Multiple 3D object tracking for augmented reality, Proceedings of the 7th IEEE/ACM International Symposium on Mixed and Augmented Reality, pp.117-120, 2008.

F. Leberl, A. Irschara, and T. Pock, Point clouds". In: Photogrammetric Engineering & Remote Sensing, vol.76, p.1, 2010.

A. Nègre, L. Rummelhard, and C. Laugier, Hybrid Sampling Bayesian Occupancy Filter, IEEE Intelligent Vehicles Symposium (IV), p.1, 2014.

L. Rummelhard, A. Negre, and C. Laugier, Conditional Monte Carlo Dense Occupancy Tracker, 18th IEEE International Conference on Intelligent Transportation Systems. Las Palmas, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01205298

J. Rios-martinez, A. Spalanzani, and C. Laugier, From Proxemics Theory to Socially-Aware Navigation: A Survey, International Journal of Social Robotics, vol.7, issue.2, pp.137-153, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01067278

Ö. Erkent, C. Wolf, C. Laugier, D. S. Gonzalez, and V. Cano, Semantic Grid Estimation with a Hybrid Bayesian and Deep Neural Network Approach, IEEE/RSJ International Conference on Intelligent Robots and Systems, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01881377

Z. Muhammad-zeeshan, M. Stark, and K. Schindler, Are cars just 3d boxes?-jointly estimating the 3d shape of multiple objects, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.3678-3685, 2014.

M. Simon, S. Milz, K. Amende, and H. Gross, Complex-YOLO: Real-time 3D Object Detection on Point Clouds, p.3, 2018.

B. Wu, A. Wan, X. Yue, and K. Keutzer, Squeezeseg: Convolutional neural nets with recurrent crf for real-time road-object segmentation from 3d lidar point cloud, 2017.

M. Engelcke, D. Rao, D. Z. Wang, C. H. Tong, and I. Posner, Vote3deep: Fast object detection in 3d point clouds using efficient convolutional neural networks, 2017 IEEE International Conference on, pp.1355-1361, 2017.

D. Maturana and S. Scherer, Voxnet: A 3d convolutional neural network for real-time object recognition, Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on, p.3, 2015.

G. Riegler, A. Osman-ulusoy, and A. Geiger, Octnet: Learning deep 3d representations at high resolutions, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol.3, p.3, 2017.

P. Wang, Y. Liu, Y. Guo, C. Sun, and X. Tong, O-cnn: Octree-based convolutional neural networks for 3d shape analysis, ACM Transactions on Graphics (TOG), vol.36, issue.4, p.3, 2017.

X. Chen, K. Kundu, and Z. Zhang, Monocular 3d object detection for autonomous driving, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.2147-2156, 2016.

X. Chen, H. Ma, J. Wan, B. Li, and T. Xia, Multi-view 3d object detection network for autonomous driving, IEEE CVPR, vol.1, p.41, 2017.

H. Charles-r-qi, K. Su, L. J. Mo, and . Guibas, Pointnet: Deep learning on point sets for 3d classification and segmentation, Proc. Computer Vision and Pattern Recognition (CVPR)

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

. Binh-son, M. Hua, S. Tran, and . Yeung, Pointwise convolutional neural networks, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol.4, p.35, 2018.

Y. Li, R. Bu, M. Sun, and B. Chen, PointCNN, vol.4, 2018.

F. Engelmann, T. Kontogianni, A. Hermans, and B. Leibe, Exploring spatial context for 3d semantic segmentation of point clouds, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.716-724, 2017.

C. R. Qi, W. Liu, C. Wu, H. Su, and L. J. Guibas, Frustum PointNets for 3D Object Detection From RGB-D Data, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.

Y. Zhou and O. Tuzel, Voxelnet: End-to-end learning for point cloud based 3d object detection, 2017.

S. Itzik-ben, 3D Point Cloud Classification using Deep Learning -Recent Works

M. Jaderberg, K. Simonyan, and A. Zisserman, Spatial transformer networks, Advances in neural information processing systems, pp.2017-2025, 2015.

K. Shaoqing-ren, R. He, J. Girshick, and . Sun, Faster r-cnn: Towards real-time object detection with region proposal networks, Advances in neural information processing systems, p.13, 2015.

J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, You only look once: Unified, real-time object detection, Proceedings of the IEEE conference on computer vision and pattern recognition, p.14, 2016.

W. Liu, D. Anguelov, and D. Erhan, Ssd: Single shot multibox detector, European conference on computer vision, p.14, 2016.

. John-k-tsotsos, Analyzing vision at the complexity level, Behavioral and brain sciences, vol.13, pp.423-445, 1990.

. Ra-rensink, The dynamic representation of scenes. Visual Cognition7: 1742. Visual search for change: A probe into the nature of attentional processing, Visual Cognition, vol.7, p.14, 2000.

V. Mnih, N. Heess, and A. Graves, Recurrent models of visual attention, Advances in neural information processing systems, pp.2204-2212, 2014.

J. Ba, V. Mnih, and K. Kavukcuoglu, Multiple object recognition with visual attention, 2014.

A. Ablavatski, S. Lu, and J. Cai, Enriched deep recurrent visual attention model for multiple object recognition, Applications of Computer Vision (WACV, vol.25, pp.17-19, 2017.

. Søren-kaae, C. K. Sønderby, L. Sønderby, O. Maaløe, and . Winther, Recurrent spatial transformer networks, vol.37, pp.17-19, 2015.

K. Gregor, I. Danihelka, A. Graves, D. Jimenez-rezende, and D. Wierstra, Draw: A recurrent neural network for image generation, p.19, 2015.

J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. cite arxiv:1412.3555Comment: Presented in NIPS 2014 Deep Learning and Representation Learning Workshop, p.19, 2014.

F. Baradel, C. Wolf, J. Mille, and G. Taylor, Glimpse clouds: Human activity recognition from unstructured feature points, Computer Vision and Pattern Recognition (CVPR), vol.3, p.19, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01713109

H. Chu, W. Kundu, R. Urtasun, and S. Fidler, SurfConv: Bridging 3D and 2D Convolution for RGBD Images, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, p.25, 2018.

S. Xie, S. Liu, Z. Chen, and Z. Tu, Attentional ShapeCon-textNet for Point Cloud Recognition, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p.25, 2018.

Z. Wu, S. Song, and A. Khosla, 3d shapenets: A deep representation for volumetric shapes, Proceedings of the IEEE conference on computer vision and pattern recognition, p.29, 2015.

A. Geiger, P. Lenz, and R. Urtasun, Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite, Conference on Computer Vision and Pattern Recognition (CVPR). 2012 (cit, vol.41, p.30

X. Chen, K. Kundu, and Y. Zhu, 3d object proposals for accurate object class detection, Advances in Neural Information Processing Systems, p.42, 2015.

B. Li, 3D fully convolutional network for vehicle detection in point cloud, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), p.42, 2017.

A. Pointnet and ;. .. , 2 2.1 (a) Shows the principle of working of LiDAR; (b) Typical point cloud generated by high-definition LiDAR; (c) Point cloud generated from stereo cameras, Multiple object detection in 3D point clouds

]. .. and ]. .. , 12 2.5 (a)Spherical point-wise convolution operator centred at different points in a point cloud; (b) Convolutions in images; (c) Pointwise kernel and subdomains; Images reproduced from ACFR dataset and guide to arithmetic convolution

]. .. , 16 2.9 Architecture of Spatial Transformer Network

. Network and . .. Pointnet, 20 3.2 Architecture of Context Network; MLP -Multi-Layer Perceptron 21

, 2D illustration of working of 3D Transformer, p.24

. .. , Sample images of cluttered MNIST dataset, p.28

. .. Dataset, Sample images of ModelNet40 [41, p.29

. Acfr-sydney-urban and . .. Dataset, 30 4.4 30m x 30m Working area in blue rectangle, 10m x 10m crops in red squares

K. Augmented and . Dataset, each cropped region has 3 bounding boxes. The green bounding boxes represent the locations of car and red bounding boxes are random locations of non-car regions, p.32

, Autonomous vehicle platform

, Shows partitioning of point cloud by PointWise kernel into subdomains represented by different color. Points within a subdomain share same weight, ModelNet40 dataset, p.37

, Recurrent-STN Network with supervised localization, p.38

, Recurrent-STN Network without supervised localization, p.38

A. Pointnet, Each row shows a sequence of the predictions by the network. First four rows illustrates scenarios where network successfully identified and localised the car. Whereas last two rows shows failure cases, p.44

, Attentional PointNet on Team Chroma Inria dataset, vol.45

, 36 5.2 Performance comparison in 3D detection: average precision (in %) on KITTI validation set. BV -Birdeye View