Built on

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 , 2014
Original
2014
Earlier work this paper cites.
I. Loshchilov and F. Hutter, “SGDR: Stochastic gradient descent with warm restarts,” arXiv preprint arXiv:1608.03983 , 2016
Original
2016
Earlier work this paper cites.
I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” arXiv preprint arXiv:1711.05101 , 2017
Original
2017
Earlier work this paper cites.
Q. Chen, S. Tang, Q. Yang, and S. Fu, “COOPER: Cooperative perception for connected autonomous vehicles based on 3D point clouds,” in Proc. Int. Conf. Distrib. Comput. Syst. , 2019, pp. 514–524
2019
Earlier work this paper cites.
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
Earlier work this paper cites.
Y. Lee, J.-W. Hwang, S. Lee, Y. Bae, and J. Park, “An energy and gpu-computation efficient backbone network for real-time object detection,” in CVPRW , 2019, pp. 0–0
2019
Earlier work this paper cites.
Y.-C. Liu, J. Tian, N. Glaser, and Z. Kira, “When2com: Multi-agent perception via communication graph grouping,” in CVPR , 2020, pp. 4106–4115
2020
Earlier work this paper cites.
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 ECCV , 2020, pp. 605–621
2020
Earlier work this paper cites.
N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” in ECCV , 2020, pp. 213–229
2020
Earlier work this paper cites.
A. Caillot, S. Ouerghi, P. Vasseur, R. Boutteau, and Y. Dupuis, “Survey on cooperative perception in an automotive context,” IEEE Trans. Intell. Transp. Syst. , vol. 23, no. 9, pp. 14 204–14 223, 2022
2022
Earlier work this paper cites.
H. Yu, Y. Luo, M. Shu, Y. Huo, Z. Yang, Y. Shi, Z. Guo, H. Li, X. Hu, J. Yuan, and Z. Nie, “DAIR-V2X: A large-scale dataset for vehicle-infrastructure cooperative 3D object detection,” in CVPR , 2022, pp. 21 361–21 370
2022
Earlier work this paper cites.

Similar

Z. Bai, G. Wu, M. J. Barth, Y. Liu, E. A. Sisbot, K. Oguchi, and Z. Huang, “A survey and framework of cooperative perception: From heterogeneous singleton to hierarchical cooperation,” arXiv preprint arXiv:2208.10590 , 2022
Original
2022
Cited alongside, same era.
E. Arnold, M. Dianati, R. de Temple, and S. Fallah, “Cooperative perception for 3D object detection in driving scenarios using infrastructure sensors,” IEEE Trans. Intell. Transp. Syst. , vol. 23, no. 3, pp. 1852–1864, 2022
2022
Cited alongside, same era.
J. Cui, H. Qiu, D. Chen, P. Stone, and Y. Zhu, “COOPERNAUT: end-to-end driving with cooperative perception for networked vehicles,” in CVPR , 2022, pp. 17 252–17 262
2022
Cited alongside, same era.
R. Xu, H. Xiang, Z. Tu, X. Xia, M.-H. Yang, and J. Ma, “V2X-ViT: Vehicle-to-everything cooperative perception with vision transformer,” in ECCV , 2022, pp. 107–124
2022
Cited alongside, same era.

Then

T. Zhang, X. Chen, Y. Wang, Y. Wang, and H. Zhao, “MUTR3D: A multi-camera tracking framework via 3D-to2D queries,” in CVPRW , 2022, pp. 4537–4546
2022
Later among the works it cites.
Y. Han, H. Zhang, H. Li, Y. Jin, C. Lang, and Y. Li, “Collaborative perception in autonomous driving: Methods, datasets and challenges,” arXiv preprint arXiv:2301.06262 , 2023
Original
2023
Closest in time.
S. Fan, Z. Wang, X. Huo, Y. Wang, and J. Liu, “Calibration-free BEV representation for infrastructure perception,” arXiv preprint arXiv:2303.03583 , 2023
Original
2023
Closest in time.
Z. Wang, S. Fan, X. Huo, T. Xu, Y. Wang, J. Liu, Y. Chen, and Y.-Q. Zhang, “VIMI: Vehicle-infrastructure multi-view intermediate fusion for camera-based 3D object detection,” arXiv preprint arXiv:2303.10975 , 2023
Original
2023
Closest in time.

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Reading the bibliography…

2023

QUEST: Query Stream for Practical Cooperative Perception

Fan, Siqi, Yu, Haibao, Yang, Wenxian et al.

Understand

Cooperative perception can effectively enhance individual perception performance by providing additional viewpoint and expanding the sensing field.

Existing cooperation paradigms are either interpretable (result cooperation) or flexible (feature cooperation).
In this paper, we propose the concept of query cooperation to enable interpretable instance-level flexible feature interaction.
To specifically explain the concept, we propose a cooperative perception framework, termed QUEST, which let query stream flow among agents.

Built on

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 , 2014
Original
2014
Earlier work this paper cites.
I. Loshchilov and F. Hutter, “SGDR: Stochastic gradient descent with warm restarts,” arXiv preprint arXiv:1608.03983 , 2016
Original
2016
Earlier work this paper cites.
I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” arXiv preprint arXiv:1711.05101 , 2017
Original
2017
Earlier work this paper cites.
Q. Chen, S. Tang, Q. Yang, and S. Fu, “COOPER: Cooperative perception for connected autonomous vehicles based on 3D point clouds,” in Proc. Int. Conf. Distrib. Comput. Syst. , 2019, pp. 514–524
2019
Earlier work this paper cites.
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
Earlier work this paper cites.
Y. Lee, J.-W. Hwang, S. Lee, Y. Bae, and J. Park, “An energy and gpu-computation efficient backbone network for real-time object detection,” in CVPRW , 2019, pp. 0–0
2019
Earlier work this paper cites.
Y.-C. Liu, J. Tian, N. Glaser, and Z. Kira, “When2com: Multi-agent perception via communication graph grouping,” in CVPR , 2020, pp. 4106–4115
2020
Earlier work this paper cites.
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 ECCV , 2020, pp. 605–621
2020
Earlier work this paper cites.
N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” in ECCV , 2020, pp. 213–229
2020
Earlier work this paper cites.
A. Caillot, S. Ouerghi, P. Vasseur, R. Boutteau, and Y. Dupuis, “Survey on cooperative perception in an automotive context,” IEEE Trans. Intell. Transp. Syst. , vol. 23, no. 9, pp. 14 204–14 223, 2022
2022
Earlier work this paper cites.
H. Yu, Y. Luo, M. Shu, Y. Huo, Z. Yang, Y. Shi, Z. Guo, H. Li, X. Hu, J. Yuan, and Z. Nie, “DAIR-V2X: A large-scale dataset for vehicle-infrastructure cooperative 3D object detection,” in CVPR , 2022, pp. 21 361–21 370
2022
Earlier work this paper cites.

Similar

Z. Bai, G. Wu, M. J. Barth, Y. Liu, E. A. Sisbot, K. Oguchi, and Z. Huang, “A survey and framework of cooperative perception: From heterogeneous singleton to hierarchical cooperation,” arXiv preprint arXiv:2208.10590 , 2022
Original
2022
Cited alongside, same era.
E. Arnold, M. Dianati, R. de Temple, and S. Fallah, “Cooperative perception for 3D object detection in driving scenarios using infrastructure sensors,” IEEE Trans. Intell. Transp. Syst. , vol. 23, no. 3, pp. 1852–1864, 2022
2022
Cited alongside, same era.
J. Cui, H. Qiu, D. Chen, P. Stone, and Y. Zhu, “COOPERNAUT: end-to-end driving with cooperative perception for networked vehicles,” in CVPR , 2022, pp. 17 252–17 262
2022
Cited alongside, same era.
R. Xu, H. Xiang, Z. Tu, X. Xia, M.-H. Yang, and J. Ma, “V2X-ViT: Vehicle-to-everything cooperative perception with vision transformer,” in ECCV , 2022, pp. 107–124
2022
Cited alongside, same era.

Then

T. Zhang, X. Chen, Y. Wang, Y. Wang, and H. Zhao, “MUTR3D: A multi-camera tracking framework via 3D-to2D queries,” in CVPRW , 2022, pp. 4537–4546
2022
Later among the works it cites.
Y. Han, H. Zhang, H. Li, Y. Jin, C. Lang, and Y. Li, “Collaborative perception in autonomous driving: Methods, datasets and challenges,” arXiv preprint arXiv:2301.06262 , 2023
Original
2023
Closest in time.
S. Fan, Z. Wang, X. Huo, Y. Wang, and J. Liu, “Calibration-free BEV representation for infrastructure perception,” arXiv preprint arXiv:2303.03583 , 2023
Original
2023
Closest in time.
Z. Wang, S. Fan, X. Huo, T. Xu, Y. Wang, J. Liu, Y. Chen, and Y.-Q. Zhang, “VIMI: Vehicle-infrastructure multi-view intermediate fusion for camera-based 3D object detection,” arXiv preprint arXiv:2303.10975 , 2023
Original
2023
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Y. Hu, S. Fang, Z. Lei, Y. Zhong, and S. Chen, “Where2comm: Communication-efficient collaborative perception via spatial confidence maps,” arXiv preprint arXiv:2209.12836 , 2022

Original

2022

Cited alongside, same era.

Y. Wang, V. C. Guizilini, T. Zhang, Y. Wang, H. Zhao, and J. Solomon, “DETR3D: 3D object detection from multi-view images via 3D-to-2D queries,” in CoRL , 2022, pp. 180–191

2022

Cited alongside, same era.

F. Zeng, B. Dong, Y. Zhang, T. Wang, X. Zhang, and Y. Wei, “MOTR: End-to-end multiple-object tracking with transformer,” in ECCV , 2022, pp. 659–675

2022

Cited alongside, same era.

Y. Liu, T. Wang, X. Zhang, and J. Sun, “PETR: Position embedding transformation for multi-view 3D object detection,” in ECCV , 2022, pp. 531–548

2022

Cited alongside, same era.

Z. Li, W. Wang, H. Li, E. Xie, C. Sima, T. Lu, Y. Qiao, and J. Dai, “BEVFormer: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers,” in ECCV , 2022, pp. 1–18

2022

Cited alongside, same era.

T. Meinhardt, A. Kirillov, L. Leal-Taixe, and C. Feichtenhofer, “Trackformer: Multi-object tracking with transformers,” in CVPR , 2022, pp. 8844–8854

2022

Cited alongside, same era.

Y. Hu, Y. Lu, R. Xu, W. Xie, S. Chen, and Y. Wang, “Collaboration helps camera overtake lidar in 3D detection,” in CVPR , 2023, pp. 9243–9252

2023

Closest in time.

H. Yu, Y. Tang, E. Xie, J. Mao, J. Yuan, P. Luo, and Z. Nie, “Vehicle-infrastructure cooperative 3d object detection via feature flow prediction,” arXiv preprint arXiv:2303.10552 , 2023

Original

2023

Closest in time.

C. Yang, Y. Chen, H. Tian, C. Tao, X. Zhu, Z. Zhang, G. Huang, H. Li, Y. Qiao, L. Lu, J. Zhou, and J. Dai, “BEVFormer v2: Adapting modern image backbones to bird’s-eye-view recognition via perspective supervision,” in CVPR , 2023, pp. 17 830–17 839

2023

Closest in time.

Z. Pang, J. Li, P. Tokmakov, D. Chen, S. Zagoruyko, and Y.-X. Wang, “Standing between past and future: Spatio-temporal modeling for multi-camera 3D multi-object tracking,” in CVPR , 2023, pp. 17 928–17 938

2023

Closest in time.

H. Yu, W. Yang, H. Ruan, Z. Yang, Y. Tang, X. Gao, X. Hao, Y. Shi, Y. Pan, N. Sun, J. Song, J. Yuan, P. Luo, and Z. Nie, “V2X-Seq: A large-scale sequential dataset for vehicle-infrastructure cooperative perception and forecasting,” in CVPR , 2023, pp. 5486–5495

2023

Closest in time.

Y. Hu, J. Yang, L. Chen, K. Li, C. Sima, X. Zhu, S. Chai, S. Du, T. Lin, W. Wang, L. Lu, X. Jia, Q. Liu, J. Dai, Y. Qiao, and H. Li, “Planning-oriented autonomous driving,” in CVPR , 2023, pp. 17 853–17 862

2023

Closest in time.