Fetching the paper…
Reading the bibliography…
3D lanes offer a more comprehensive understanding of the road surface geometry than 2D lanes, thereby providing crucial references for driving decisions and trajectory planning.
A. Roberts, “Curvature attributes and their application to 3 d interpreted horizons,” First break , vol. 19, no. 2, pp. 85–100, 2001
2001
Earlier work this paper cites.
T. Blu, P. Thévenaz, and M. Unser, “Linear interpolation revitalized,” IEEE Transactions on Image Processing , vol. 13, no. 5, pp. 710–719, 2004
2004
Earlier work this paper cites.
A. Bar Hillel, R. Lerner, D. Levi, and G. Raz, “Recent progress in road and lane detection: a survey,” Machine vision and applications , vol. 25, no. 3, pp. 727–745, 2014
2014
Earlier work this paper cites.
R. Girshick, “Fast r-cnn,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 1440–1448
2015
Earlier work this paper cites.
B. Ranft and C. Stiller, “The role of machine vision for intelligent vehicles,” IEEE Transactions on Intelligent vehicles , vol. 1, no. 1, pp. 8–19, 2016
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Earlier work this paper cites.
T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 2117–2125
2017
Earlier work this paper cites.
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2980–2988
2017
Earlier work this paper cites.
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
Earlier work this paper cites.
2019
Earlier work this paper cites.
L. Reiher, B. Lampe, and L. Eckstein, “A sim2real deep learning approach for the transformation of images from multiple vehicle-mounted cameras to a semantically segmented image in bird’s eye view,” in 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) . IEEE, 2020, pp. 1–7
2020
Earlier work this paper cites.
Y. Liu, H. Chen, C. Shen, T. He, L. Jin, and L. Wang, “Abcnet: Real-time scene text spotting with adaptive bezier-curve network,” in proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 9809–9818
2020
Earlier work this paper cites.
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 Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXI 16 . Springer, 2020, pp. 666–681
2020
Cited alongside, same era.
S. Zhang, C. Chi, Y. Yao, Z. Lei, and S. Z. Li, “Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 9759–9768
2020
Cited alongside, same era.
2020
Cited alongside, same era.
N. Gosala and A. Valada, “Bird’s-eye-view panoptic segmentation using monocular frontal view images,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 1968–1975, 2022
2022
Later among the works it cites.
Y. Sun, J. Li, X. Xu, and Y. Shi, “Adaptive multi-lane detection based on robust instance segmentation for intelligent vehicles,” IEEE Transactions on Intelligent Vehicles , vol. 8, no. 1, pp. 888–899, 2022
2022
Later among the works it cites.
Q. Li, Y. Wang, Y. Wang, and H. Zhao, “Hdmapnet: An online hd map construction and evaluation framework,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 4628–4634
2022
Later among the works it cites.
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2020
Cited alongside, same era.
J. Tang, S. Li, and P. Liu, “A review of lane detection methods based on deep learning,” Pattern Recognition , vol. 111, p. 107623, 2021
2021
Cited alongside, same era.
P. Lu, C. Cui, S. Xu, H. Peng, and F. Wang, “Super: A novel lane detection system,” IEEE Transactions on Intelligent Vehicles , vol. 6, no. 3, pp. 583–593, 2021
2021
Cited alongside, same era.
J. Mao, Y. Xue, M. Niu, H. Bai, J. Feng, X. Liang, H. Xu, and C. Xu, “Voxel transformer for 3d object detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 3164–3173
2021
Cited alongside, same era.
L. Tabelini, R. Berriel, T. M. Paixao, C. Badue, A. F. De Souza, and T. Oliveira-Santos, “Keep your eyes on the lane: Real-time attention-guided lane detection,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021, pp. 294–302
2021
Cited alongside, same era.
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
Cited alongside, same era.
L. Chen, Y. Li, C. Huang, B. Li, Y. Xing, D. Tian, L. Li, Z. Hu, X. Na, Z. Li et al. , “Milestones in autonomous driving and intelligent vehicles: Survey of surveys,” IEEE Transactions on Intelligent Vehicles , vol. 8, no. 2, pp. 1046–1056, 2022
2022
Cited alongside, same era.
2022
Cited alongside, same era.
L. Chen, C. Sima, Y. Li, Z. Zheng, J. Xu, X. Geng, H. Li, C. He, J. Shi, Y. Qiao et al. , “Persformer: 3d lane detection via perspective transformer and the openlane benchmark,” in European Conference on Computer Vision . Springer, 2022, pp. 550–567
2022
Later among the works it cites.
Z. Feng, S. Guo, X. Tan, K. Xu, M. Wang, and L. Ma, “Rethinking efficient lane detection via curve modeling,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 17 062–17 070
2022
Later among the works it cites.
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
Later among the works it cites.
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 European conference on computer vision . Springer, 2022, pp. 1–18
2022
Later among the works it cites.
Y. Liu, T. Yuan, Y. Wang, Y. Wang, and H. Zhao, “Vectormapnet: End-to-end vectorized hd map learning,” in International Conference on Machine Learning . PMLR, 2023, pp. 22 352–22 369
2023
Later among the works it cites.
Y. Bai, Z. Chen, Z. Fu, L. Peng, P. Liang, and E. Cheng, “Curveformer: 3d lane detection by curve propagation with curve queries and attention,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 7062–7068
2023
Later among the works it cites.