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Recently, Vehicle-to-Everything(V2X) cooperative perception has attracted increasing attention.
X. Chen, H. Ma, J. Wan, B. Li, and T. Xia, “Multi-view 3d object detection network for autonomous driving,” in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition , 2017, pp. 1907–1915
1915
Earlier work this paper cites.
A. Rauch, F. Klanner, R. Rasshofer, and K. Dietmayer, “Car2x-based perception in a high-level fusion architecture for cooperative perception systems,” in Intelligent Vehicles Symposium , 2012
2012
Earlier work this paper cites.
Y. Xiang, R. Mottaghi, and S. Savarese, “Beyond pascal: A benchmark for 3d object detection in the wild,” in IEEE winter conference on applications of computer vision , 2014
2014
Earlier work this paper cites.
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,” in 2017 IEEE International Conference on Robotics and Automation (ICRA) , 2017
2017
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C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2017
2017
Earlier work this paper cites.
Z. Yin and O. Tuzel, “Voxelnet: End-to-end learning for point cloud based 3d object detection,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2017
2017
Earlier work this paper cites.
S. U. Bhover, A. Tugashetti, and P. Rashinkar, “V2x communication protocol in vanet for co-operative intelligent transportation system,” in 2017 International Conference on Innovative Mechanisms for Industry Applications (ICIMIA) . IEEE, 2017, pp. 602–607
2017
Earlier work this paper cites.
A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, “Carla: An open urban driving simulator,” in Conference on robot learning . PMLR, 2017, pp. 1–16
2017
Earlier work this paper cites.
J. Dybedal and G. Hovland, “Optimal placement of 3d sensors considering range and field of view,” in 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM) . IEEE, 2017, pp. 1588–1593
2017
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M. Simon, S. Milz, K. Amende, and H. M. Gross, “Complex-yolo: Real-time 3d object detection on point clouds,” arXiv preprint arXiv.1803.06199 , 2018
2018
Earlier work this paper cites.
W. Ali, S. Abdelkarim, M. Zahran, M. Zidan, and A. E. Sallab, “Yolo3d: End-to-end real-time 3d oriented object bounding box detection from lidar point cloud,” in ECCV 2018: ”3D Reconstruction meets Semantics” workshop , 2018
2018
Earlier work this paper cites.
C. Olaverri-Monreal, J. Errea-Moreno, A. Díaz-Álvarez, C. Biurrun-Quel, L. Serrano-Arriezu, and M. Kuba, “Connection of the sumo microscopic traffic simulator and the unity 3d game engine to evaluate v2x communication-based systems,” Sensors , vol. 18, no. 12, p. 4399, 2018
2018
Earlier work this paper cites.
H. Gao, B. Cheng, J. Wang, K. Li, J. Zhao, and D. Li, “Object classification using cnn-based fusion of vision and lidar in autonomous vehicle environment,” IEEE Transactions on Industrial Informatics , vol. 14, no. 9, pp. 4224–4231, 2018
2018
Earlier work this paper cites.
A. H. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang, and O. Beijbom, “Pointpillars: Fast encoders for object detection from point clouds,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 12 697–12 705
2019
Cited alongside, same era.
L. Caccia, H. Van Hoof, A. Courville, and J. Pineau, “Deep generative modeling of lidar data,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2019, pp. 5034–5040
2019
Cited alongside, same era.
M. Hahner, D. Dai, C. Sakaridis, J.-N. Zaech, and L. Van Gool, “Semantic understanding of foggy scenes with purely synthetic data,” in 2019 IEEE Intelligent Transportation Systems Conference (ITSC) . IEEE, 2019, pp. 3675–3681
2019
Cited alongside, same era.
L. Caltagirone, M. Bellone, L. Svensson, and M. Wahde, “Lidar–camera fusion for road detection using fully convolutional neural networks,” Robotics and Autonomous Systems , vol. 111, pp. 125–131, 2019
2019
R. R. Kini, “Sensor position optimization for multiple lidars in autonomous vehicles,” 2020
2020
Later among the works it cites.
G. Chen, F. Lu, Z. Li, Y. Liu, J. Dong, J. Zhao, J. Yu, and A. Knoll, “Pole-curb fusion based robust and efficient autonomous vehicle localization system with branch-and-bound global optimization and local grid map method,” IEEE Transactions on Vehicular Technology , vol. 70, no. 11, pp. 11 283–11 294, 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
M. Hahner, C. Sakaridis, D. Dai, and L. Van Gool, “Fog simulation on real lidar point clouds for 3d object detection in adverse weather,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 15 283–15 292
2021
Later among the works it cites.
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Cited alongside, same era.
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 Proceedings of the 4th ACM/IEEE Symposium on Edge Computing , 2019, pp. 88–100
2019
Cited alongside, same era.
Z. Liu, M. Arief, and D. Zhao, “Where should we place lidars on the autonomous vehicle?-an optimal design approach,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 2793–2799
2019
Cited alongside, same era.
T.-H. Kim and T.-H. Park, “Placement optimization of multiple lidar sensors for autonomous vehicles,” IEEE Transactions on Intelligent Transportation Systems , vol. 21, no. 5, pp. 2139–2145, 2019
2019
Cited alongside, same era.
R. Li, X. Li, C.-W. Fu, D. Cohen-Or, and P.-A. Heng, “Pu-gan: a point cloud upsampling adversarial network,” in Proceedings of the IEEE/CVF international conference on computer vision , 2019, pp. 7203–7212
2019
Cited alongside, same era.
S. Manivasagam, S. Wang, K. Wong, W. Zeng, M. Sazanovich, S. Tan, B. Yang, W.-C. Ma, and R. Urtasun, “Lidarsim: Realistic lidar simulation by leveraging the real world,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 11 167–11 176
2020
Cited alongside, same era.
T.-H. Wang, S. Manivasagam, M. Liang, B. Yang, W. Zeng, and R. Urtasun, “V2vnet: Vehicle-to-vehicle communication for joint perception and prediction,” in European Conference on Computer Vision . Springer, 2020, pp. 605–621
2020
Cited alongside, same era.
G. Melotti, C. Premebida, and N. Gonçalves, “Multimodal deep-learning for object recognition combining camera and lidar data,” in 2020 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC) . IEEE, 2020, pp. 177–182
2020
Cited alongside, same era.
C. Fu, C. Dong, C. Mertz, and J. M. Dolan, “Depth completion via inductive fusion of planar lidar and monocular camera,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 10 843–10 848
2020
Cited alongside, same era.
Y. Li, S. Ren, P. Wu, S. Chen, C. Feng, and W. Zhang, “Learning distilled collaboration graph for multi-agent perception,” Advances in Neural Information Processing Systems , vol. 34, pp. 29 541–29 552, 2021
2021
Later among the works it cites.
2022
Closest in time.
H. Hu, Z. Liu, S. Chitlangia, A. Agnihotri, and D. Zhao, “Investigating the impact of multi-lidar placement on object detection for autonomous driving,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 2550–2559
2022
Closest in time.
M. Hahner, C. Sakaridis, M. Bijelic, F. Heide, F. Yu, D. Dai, and L. Van Gool, “Lidar snowfall simulation for robust 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 16 364–16 374
2022
Closest in time.
R. Xu, H. Xiang, X. Xia, X. Han, J. Li, and J. Ma, “Opv2v: An open benchmark dataset and fusion pipeline for perception with vehicle-to-vehicle communication,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 2583–2589
2022
Closest in time.
2022
Closest in time.
S. Su, Y. Li, S. He, S. Han, C. Feng, C. Ding, and F. Miao, “Uncertainty quantification of collaborative detection for self-driving,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 5588–5594
2023
Closest in time.
R. Xu, H. Xiang, X. Han, X. Xia, Z. Meng, C.-J. Chen, C. Correa-Jullian, and J. Ma, “The opencda open-source ecosystem for cooperative driving automation research,” IEEE Transactions on Intelligent Vehicles , vol. 8, no. 4, pp. 2698–2711, 2023
2023
Closest in time.