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3D lane detection is an integral part of autonomous driving systems.
2014
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2017
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D. Neven, B. De Brabandere, S. Georgoulis, M. Proesmans, and L. Van Gool, “Towards end-to-end lane detection: an instance segmentation approach,” in 2018 IEEE intelligent vehicles symposium (IV) . IEEE, 2018, pp. 286–291
2018
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Y. Hou, Z. Ma, C. Liu, and C. C. Loy, “Learning lightweight lane detection cnns by self attention distillation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 1013–1021
2019
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Z. Chen, Q. Liu, and C. Lian, “Pointlanenet: Efficient end-to-end cnns for accurate real-time lane detection,” in 2019 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2019, pp. 2563–2568
2019
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X. Li, J. Li, X. Hu, and J. Yang, “Line-cnn: End-to-end traffic line detection with line proposal unit,” IEEE Transactions on Intelligent Transportation Systems , vol. 21, no. 1, pp. 248–258, 2019
2019
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N. Garnett, R. Cohen, T. Pe’er, R. Lahav, and D. Levi, “3d-lanenet: end-to-end 3d multiple lane detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 2921–2930
2019
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W. Van Gansbeke, B. De Brabandere, D. Neven, M. Proesmans, and L. Van Gool, “End-to-end lane detection through differentiable least-squares fitting,” in Proceedings of the IEEE International Conference on Computer Vision Workshops , 2019, pp. 0–0
2019
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“Waymo open dataset: An autonomous driving dataset,” 2019
2019
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M. Tan and Q. Le, “Efficientnet: Rethinking model scaling for convolutional neural networks,” in International conference on machine learning . PMLR, 2019, pp. 6105–6114
2019
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2020
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2020
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Z. Li, “Curvelane-nas: Unifying lane-sensitive architecture search and adaptive point blending,” 2020
2020
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L. Tabelini, R. Berriel, T. M. Paixo, C. Badue, A. D. Souza, and T. Oliveira-Santos, “Keep your eyes on the lane: Real-time attention-guided lane detection,” 2020
2020
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2020
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2020
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Y. Guo, G. Chen, P. Zhao, W. Zhang, J. Miao, J. Wang, and T. E. Choe, “Gen-lanenet: A generalized and scalable approach for 3d lane detection,” in European Conference on Computer Vision . Springer, 2020, pp. 666–681
R. Liu, Z. Yuan, T. Liu, and Z. Xiong, “End-to-end lane shape prediction with transformers,” in Proceedings of the IEEE/CVF winter conference on applications of computer vision , 2021, pp. 3694–3702
2021
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Y. B. Can, A. Liniger, D. P. Paudel, and L. Van Gool, “Structured bird’s-eye-view traffic scene understanding from onboard images,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 15 661–15 670
2021
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J. Wang, Y. Ma, S. Huang, T. Hui, F. Wang, C. Qian, and T. Zhang, “A keypoint-based global association network for lane detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 1392–1401
2022
Closest in time.
T. Zheng, Y. Huang, Y. Liu, W. Tang, Z. Yang, D. Cai, and X. He, “Clrnet: Cross layer refinement network for lane detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 898–907
2022
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2020
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2020
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N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” in European conference on computer vision . Springer, 2020, pp. 213–229
2020
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2020
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2020
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B. Wang, Z. Wang, and Y. Zhang, “Polynomial regression network for variable-number lane detection,” in European Conference on Computer Vision . Springer, 2020, pp. 719–734
2020
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2020
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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,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2020
2020
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F. Yan, M. Nie, X. Cai, J. Han, H. Xu, Z. Yang, C. Ye, Y. Fu, M. B. Mi, and L. Zhang, “Once-3dlanes: Building monocular 3d lane detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 17 143–17 152
2022
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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 Conference on Robot Learning . PMLR, 2022, pp. 180–191
2022
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2022
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R. Liu, D. Chen, T. Liu, Z. Xiong, and Z. Yuan, “Learning to predict 3d lane shape and camera pose from a single image via geometry constraints,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 36, no. 2, 2022, pp. 1765–1772
2022
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L. Chen, C. Sima, Y. Li, Z. Zheng, J. Xu, X. Geng, H. Li, C. He, J. Shi, Y. Qiao, and J. Yan, “Persformer: 3d lane detection via perspective transformer and the openlane benchmark,” in European Conference on Computer Vision (ECCV) , 2022
2022
Closest in time.
2022
Closest in time.