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3D object detection is a common function within the perception system of an autonomous vehicle and outputs a list of 3D bounding boxes around objects of interest.
Z. Yang, Y. Sun, S. Liu, X. Shen, and J. Jia, “Std: Sparse-to-dense 3d object detector for point cloud,” in The IEEE International Conference on Computer Vision (ICCV) , 2019, pp. 1951–1960
1960
Earlier work this paper cites.
R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision , 2nd ed. Cambridge University Press, 2004
2004
Earlier work this paper cites.
P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan, “Object detection with discriminatively trained part-based models,” IEEE transactions on pattern analysis and machine intelligence , vol. 32, no. 9, pp. 1627–1645, 2009
2009
Earlier work this paper cites.
C. Glennie and D. D. Lichti, “Static calibration and analysis of the velodyne hdl-64e s2 for high accuracy mobile scanning,” Remote Sensing , vol. 2, no. 6, pp. 1610–1624, 2010
2010
Earlier work this paper cites.
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman, “The Pascal Visual Object Classes (VOC) Challenge,” International journal of computer vision , vol. 88, no. 2, pp. 303–338, 2010
2010
Earlier work this paper cites.
R. Roberts, S. N. Sinha, R. Szeliski, and D. Steedly, “Structure from motion for scenes with large duplicate structures,” in CVPR 2011 , 2011, pp. 3137–3144
2011
Earlier work this paper cites.
A. Geiger, P. Lenz, and R. Urtasun, “Are We Ready for Autonomous Driving? The KITTI vision benchmark suite,” in 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE, 2012, pp. 3354–3361
2012
Earlier work this paper cites.
F. Castanedo, “A review of data fusion techniques,” The Scientific World Journal , vol. 2013, 2013
2013
Earlier work this paper cites.
M. Knorr, W. Niehsen, and C. Stiller, “Online extrinsic multi-camera calibration using ground plane induced homographies,” in 2013 IEEE Intelligent Vehicles Symposium (IV) , June 2013, pp. 236–241
2013
Earlier work this paper cites.
T. Collins, “STREET LIGHTING INSTALLATIONS: For Lighting on New Residential Roads and Industrial Estates,” Durham County Council Neighbourhood Services, Tech. Rep., December 2014
2014
Earlier work this paper cites.
I. Tomljenovic and A. Rousell, “Influence of point cloud density on the results of automated object-based building extraction from als data,” 2014
2014
Earlier work this paper cites.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-time Object Detection with Region Proposal Networks,” in Advances in Neural Information Processing Systems , 2015, pp. 91–99
2015
Earlier work this paper cites.
M. Everingham, S. A. Eslami, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes challenge: A retrospective,” International journal of computer vision , vol. 111, no. 1, pp. 98–136, 2015
2015
Earlier work this paper cites.
B. Li, T. Zhang, and T. Xia, “Vehicle Detection from 3d Lidar Using Fully Convolutional Network,” in Proceedings of Robotics: Science and Systems , AnnArbor, Michigan, Jun. 2016
2016
Cited alongside, same era.
“Technical report, Tesla Crash,” National Highway Traffic Safety Administration, US Department of Transportation, Tech. Rep. PE 16-007, January 2017
2017
Cited alongside, same era.
X. Chen, H. Ma, J. Wan, B. Li, and T. Xia, “Multi-View 3D Object Detection Network for Autonomous Driving,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2017
2017
Cited alongside, same era.
C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2017, pp. 652–660
2017
Cited alongside, same era.
L. E. Ortiz, E. V. Cabrera, and L. M. Gonçalves, “Depth data error modeling of the zed 3d vision sensor from stereolabs,” ELCVIA: electronic letters on computer vision and image analysis , vol. 17, no. 1, pp. 0001–15, 2018
2018
Later among the works it cites.
Y. Yan, Y. Mao, and B. Li, “SECOND: Sparsely Embedded Convolutional Detection,” Sensors , vol. 18, no. 10, p. 3337, Oct. 2018. [Online]. Available: https://www.mdpi.com/1424-8220/18/10/3337
2018
Later among the works it cites.
E. Arnold, O. Y. Al-Jarrah, M. Dianati, S. Fallah, D. Oxtoby, and A. Mouzakitis, “A Survey on 3D Object Detection Methods for Autonomous Driving Applications,” IEEE Transactions on Intelligent Transportation Systems , 2019
2019
Closest in time.
D. Tian, G. Wu, P. Hao, K. Boriboonsomsin, and M. J. Barth, “Connected vehicle-based lane selection assistance application,” IEEE Transactions on Intelligent Transportation Systems , vol. 20, no. 7, pp. 2630–2643, July 2019
2019
Closest in time.
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A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, “CARLA: An open urban driving simulator,” in Proceedings of the 1st Annual Conference on Robot Learning , 2017, pp. 1–16
2017
Cited alongside, same era.
J. V. Brummelen, M. O’Brien, D. Gruyer, and H. Najjaran, “Autonomous vehicle perception: The technology of today and tomorrow,” Transportation Research Part C: Emerging Technologies , vol. 89, pp. 384 – 406, 2018. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0968090X18302134
2018
Cited alongside, same era.
“Preliminary report, Highway HWY18MH010,” National Transportation Safety Board, US Government, Tech. Rep. HWY18MH010, May 2018
2018
Cited alongside, same era.
J. Ku, M. Mozifian, J. Lee, A. Harakeh, and S. Waslander, “Joint 3d proposal generation and object detection from view aggregation,” IROS , 2018
2018
Cited alongside, same era.
C. R. Qi, W. Liu, C. Wu, H. Su, and L. J. Guibas, “Frustum PointNets for 3D Object Detection From RGB-D Data,” in 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2018
2018
Cited alongside, same era.
S. Zhang, J. Chen, F. Lyu, N. Cheng, W. Shi, and X. Shen, “Vehicular communication networks in the automated driving era,” IEEE Communications Magazine , vol. 56, no. 9, pp. 26–32, Sep. 2018
2018
Cited alongside, same era.
J. Beltrán, C. Guindel, F. M. Moreno, D. Cruzado, F. Garcia, and A. De La Escalera, “Birdnet: a 3d object detection framework from lidar information,” in 2018 21st International Conference on Intelligent Transportation Systems (ITSC) . IEEE, 2018, pp. 3517–3523
2018
Cited alongside, same era.
M. Simony, S. Milzy, K. Amendey, and H.-M. Gross, “Complex-yolo: An euler-region-proposal for real-time 3d object detection on point clouds,” in The European Conference on Computer Vision (ECCV) Workshops , September 2018
2018
Cited alongside, same era.
A. Correa, R. Alms, J. Gozalvez, M. Sepulcre, M. Rondinone, R. Blokpoel, L. Lücken, and G. Thandavarayan, “Infrastructure support for cooperative maneuvers in connected and automated driving,” in 2019 IEEE Intelligent Vehicles Symposium (IV) , June 2019, pp. 20–25
2019
Closest in time.
E. Arnold, O. Y. Al-Jarrah, M. Dianati, S. Fallah, D. Oxtoby, and A. Mouzakitis, “Cooperative object classification for driving applications,” in 2019 IEEE Intelligent Vehicles Symposium (IV) , June 2019, pp. 2484–2489
2019
Closest in time.
2019
Closest in time.
S. Shi, X. Wang, and H. Li, “Pointrcnn: 3d object proposal generation and detection from point cloud,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2019
2019
Closest in time.
Q. Chen, S. Tang, Q. Yang, and S. Fu, “Cooper: Cooperative Perception for Connected Autonomous Vehicles based on 3d Point Clouds,” in The 39th IEEE International Conference on Distributed Computing Systems (ICDCS) , July 2019
2019
Closest in time.
Q. Chen, X. Ma, S. Tang, J. Guo, Q. Yang, and S. Fu, “F-Cooper: Feature based Cooperative Perception for Autonomous Vehicle Edge Computing System Using 3D Point Clouds,” in IEEE/ACM Symposium on Edge Computing (SEC) , November 2019
2019
Closest in time.
B. Hurl, R. Cohen, K. Czarnecki, and S. Waslander, “Trupercept: Trust modelling for autonomous vehicle cooperative perception from synthetic data,” 2019
2019
Closest in time.
P. Ghamisi, B. Rasti, N. Yokoya, Q. Wang, B. Hofle, L. Bruzzone, F. Bovolo, M. Chi, K. Anders, R. Gloaguen et al. , “Multisource and multitemporal data fusion in remote sensing: A comprehensive review of the state of the art,” IEEE Geoscience and Remote Sensing Magazine , vol. 7, no. 1, pp. 6–39, 2019
2019
Closest in time.
R. Yue, H. Xu, J. Wu, R. Sun, and C. Yuan, “Data registration with ground points for roadside lidar sensors,” Remote Sensing , vol. 11, no. 11, p. 1354, 2019
2019
Closest in time.