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Existing Vehicle-to-Everything (V2X) cooperative perception methods rely on accurate multi-agent 3D annotations.
2008
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
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,”
2014
Earlier work this paper cites.
Y. Zhou and O. Tuzel, “VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection,” 2018, pp. 4490–4499
2018
Earlier work this paper cites.
Y. Yan, Y. Mao, and B. Li, “Second: Sparsely embedded convolutional detection,”
2018
Earlier work this paper cites.
M.-F. Chang, J. Lambert, P. Sangkloy, J. Singh, S. Bak, A. Hartnett, D. Wang, P. Carr, S. Lucey, D. Ramanan, and J. Hays, “Argoverse: 3D Tracking and Forecasting With Rich Maps,” 2019, pp. 8748–8757
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
2019
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
2019
Earlier work this paper cites.
I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” in
2019
Earlier work this paper cites.
H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom, “nuscenes: A multimodal dataset for autonomous driving,” in
2020
Earlier work this paper cites.
P. Sun, H. Kretzschmar, X. Dotiwalla, A. Chouard, V. Patnaik, P. Tsui, J. Guo, Y. Zhou, Y. Chai, B. Caine, V. Vasudevan, W. Han, J. Ngiam, H. Zhao, A. Timofeev, S. Ettinger, M. Krivokon, A. Gao, A. Joshi, Y. Zhang, J. Shlens, Z. Chen, and D. Anguelov, “Scalability in Perception for Autonomous Driving: Waymo Open Dataset,” 2020, pp. 2446–2454
2020
Earlier work this paper cites.
Y. Li, S. Ren, P. Wu, S. Chen, C. Feng, and W. Zhang, “Learning distilled collaboration graph for multi-agent perception,” in
2021
Earlier work this paper cites.
K. He, X. Chen, S. Xie, Y. Li, P. Doll’ar, and R. B. Girshick, “Masked autoencoders are scalable vision learners,”
2021
Earlier work this paper cites.
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
2022
Earlier work this paper cites.
Y. Li, D. Ma, Z. An, Z. Wang, Y. Zhong, S. Chen, and C. Feng, “V2X-Sim: Multi-Agent Collaborative Perception Dataset and Benchmark for Autonomous Driving,”
2022
Earlier work this paper cites.
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
Earlier work this paper cites.
Y. Hu, S. Fang, Z. Lei, Y. Zhong, and S. Chen, “Where2comm: Communication-efficient collaborative perception via spatial confidence maps,”
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,” 2022, pp. 21 361–21 370
2022
Earlier work this paper cites.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Q. Li, Z. Peng, L. Feng, Q. Zhang, Z. Xue, and B. Zhou, “Metadrive: Composing diverse driving scenarios for generalizable reinforcement learning,”
2022
Cited alongside, same era.
C. Feichtenhofer, H. Fan, Y. Li, and K. He, “Masked autoencoders as spatiotemporal learners,”
2022
Cited alongside, same era.
P. Gao, T. Ma, H. Li, J. Dai, and Y. Qiao, “Convmae: Masked convolution meets masked autoencoders,”
Y. Li, S. Z. Zhao, C. Xu, C. Tang, C. Li, M. Ding, M. Tomizuka, and W. Zhan, “Pre-training on synthetic driving data for trajectory prediction,” 2023
2023
Later among the works it cites.
C. Min, L. Xiao, D. Zhao, Y. Nie, and B. Dai, “Occupancy-mae: Self-supervised pre-training large-scale lidar point clouds with masked occupancy autoencoders,”
2023
Later among the works it cites.
X. Tian, H. Ran, Y. Wang, and H. Zhao, “Geomae: Masked geometric target prediction for self-supervised point cloud pre-training,” in
2023
Later among the works it cites.
Q. Li, Z. Peng, L. Feng, Z. Liu, C. Duan, W. Mo, and B. Zhou, “Scenarionet: Open-source platform for large-scale traffic scenario simulation and modeling,”
2023
Later among the works it cites.
D. Huang, S. Peng, T. He, H. Yang, X. Zhou, and W. Ouyang, “Ponder: Point cloud pre-training via neural rendering,” in
2023
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2022
Cited alongside, same era.
2022
Cited alongside, same era.
X. Yu, L. Tang, Y. Rao, T. Huang, J. Zhou, and J. Lu, “Point-bert: Pre-training 3d point cloud transformers with masked point modeling,” in
2022
Cited alongside, same era.
Y. Pang, W. Wang, F. E. Tay, W. Liu, Y. Tian, and L. Yuan, “Masked autoencoders for point cloud self-supervised learning,” in
2022
Cited alongside, same era.
2023
Cited alongside, same era.
R. Xu, X. Xia, J. Li, H. Li, S. Zhang, Z. Tu, Z. Meng, H. Xiang, X. Dong, R. Song, H. Yu, B. Zhou, and J. Ma, “V2V4Real: A Real-World Large-Scale Dataset for Vehicle-to-Vehicle Cooperative Perception,” 2023, pp. 13 712–13 722
2023
Cited alongside, same era.
Y. Lu, Q. Li, B. Liu, M. Dianati, C. Feng, S. Chen, and Y. Wang, “Robust collaborative 3d object detection in presence of pose errors,” in
2023
Cited alongside, same era.
S. Wei, Y. Wei, Y. Hu, Y. Lu, Y. Zhong, S. Chen, and Y. Zhang, “Asynchrony-robust collaborative perception via bird’s eye view flow,” in
2023
Cited alongside, same era.
Later among the works it cites.
2023
Later among the works it cites.
H. Yang, T. He, J. Liu, H. Chen, B. Wu, B. Lin, X. He, and W. Ouyang, “Gd-mae: Generative decoder for mae pre-training on lidar point clouds,” in
2023
Later among the works it cites.
B. Wang, L. Zhang, Z. Wang, Y. Zhao, and T. Zhou, “Core: Cooperative reconstruction for multi-agent perception,”
2023
Later among the works it cites.
H. Xiang, Z. Zheng, X. Xia, R. Xu, L. Gao, Z. Zhou, X. Han, X. Ji, M. Li, Z. Meng,
2024
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R. Hao, S. Fan, Y. Dai, Z. Zhang, C. Li, Y. Wang, H. Yu, W. Yang, Y. Jirui, and Z. Nie, “Rcooper: A real-world large-scale dataset for roadside cooperative perception,” in
2024
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W. Zimmer, G. A. Wardana, S. Sritharan, X. Zhou, R. Song, and A. C. Knoll, “Tumtraf v2x cooperative perception dataset,” in
2024
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Y. Li, Z. Li, N. Chen, M. Gong, Z. Lyu, Z. Wang, P. Jiang, and C. Feng, “Multiagent multitraversal multimodal self-driving: Open mars dataset,” in
2024
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Z. Zheng, X. Xia, L. Gao, H. Xiang, and J. Ma, “Cooperfuse: A real-time cooperative perception fusion framework,” in
2024
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Z. Lin, Y. Wang, S. Qi, N. Dong, and M.-H. Yang, “Bev-mae: Bird’s eye view masked autoencoders for point cloud pre-training in autonomous driving scenarios,” in
2024
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C. Sautier, G. Puy, A. Boulch, R. Marlet, and V. Lepetit, “BEVContrast: Self-supervision in bev space for automotive lidar point clouds,” in
2024
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2024
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Y. Lu, Y. Hu, Y. Zhong, D. Wang, S. Chen, and Y. Wang, “An extensible framework for open heterogeneous collaborative perception,” in
2024
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