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3D-LaneNet+ is a camera-based DNN method for anchor free 3D lane detection which is able to detect 3d lanes of any arbitrary topology such as splits, merges, as well as short and perpendicular lanes.
An empirical evaluation of deep learning on highway driving
B. Huval, T. Wang, S. Tandon, J. Kiske, W. Song, J. Pazhayampallil, M. Andriluka, P. Rajpurkar, T. Migimatsu, R. Cheng-Yue, F. A. Mujica, A. Coates, and A. Y. Ng · 2015
Earlier work this paper cites.
Deeplanes: End-to-end lane position estimation using deep neural networksa
A. Gurghian, T. Koduri, S. V. Bailur, K. J. Carey, and V. N. Murali · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
Ssd: Single shot multibox detector
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg · 2016
Earlier work this paper cites.
Semantic instance segmentation with a discriminative loss function
B. D. Brabandere, D. Neven, and L. V. Gool · 2017
Earlier work this paper cites.
Vpgnet: Vanishing point guided network for lane and road marking detection and recognition
S. Lee, J. Kim, J. Shin Yoon, S. Shin, O. Bailo, N. Kim, T.-H. Lee, H. Seok Hong, S.-H. Han, and I. So Kweon · 2017
Earlier work this paper cites.
Deep multi-sensor lane detection
M. Bai, G. Mattyus, N. Homayounfar, S. Wang, S. K. Lakshmikanth, and R. Urtasun · 2018
Earlier work this paper cites.
Cornernet: Detecting objects as paired keypoints
H. Law and J. Deng · 2018
Earlier work this paper cites.
Focal loss for dense object detection
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollar · 2018
Cited alongside, same era.
A mixed classification-regression framework for 3d pose estimation from 2d images
S. Mahendran, H. Ali, and R. Vidal · 2018
Cited alongside, same era.
Towards end-to-end lane detection: an instance segmentation approach
D. Neven, B. Brabandere, S. Georgoulis, M. Proesmans, and L. Van Gool · 2018
Cited alongside, same era.
Spatial as deep: Spatial cnn for traffic scene understanding
P. Xingang, S. Jianping, L. Ping, W. Xiaogang, and T. Xiaoou · 2018
Cited alongside, same era.
3d-lanenet: end-to-end 3d multiple lane detection
N. Garnett, R. Cohen, T. Pe’er, R. Lahav, and D. Levi · 2019
Cited alongside, same era.
El-gan: Embedding loss driven generative adversarial networks for lane detection
M. Ghafoorian, C. Nugteren, N. Baka, O. Booij, and M. Hofmann · 2019
FCOS: Fully convolutional one-stage object detection
Z. Tian, C. Shen, H. Chen, and T. He · 2019
Later among the works it cites.
End-to-end lane detection through differentiable least-squares fitting
W. Van Gansbeke, B. De Brabandere, D. Neven, M. Proesmans, and L. Van Gool · 2019
Later among the works it cites.
Reppoints: Point set representation for object detection
Z. Yang, S. Liu, H. Hu, L. Wang, and S. Lin · 2019
Later among the works it cites.
Dense reppoints: Representing visual objects with dense point sets
Z. Yang, Y. Xu, H. Xue, Z. Zhang, R. Urtasun, L. Wang, S. Lin, and H. Hu · 2019
Later among the works it cites.
X. Zhou, D. Wang, and P. Krähenbühl · 2019
Later among the works it cites.
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Cited alongside, same era.
Dagmapper: Learning to map by discovering lane topology
N. Homayounfar, W.-C. Ma, J. Liang, X. Wu, J. Fan, and R. Urtasun · 2019
Cited alongside, same era.
Learning lightweight lane detection cnns by self attention distillation
Y. Hou, Z. Ma, C. Liu, and C. C. Loy · 2019
Cited alongside, same era.
Afdet: Anchor free one stage 3d object detection, 2020
R. Ge, Z. Ding, Y. Hu, Y. Wang, S. Chen, L. Huang, and Y. Li · 2020
Closest in time.
Gen-lanenet: A generalized and scalable approach for 3d lane detection
G. Yuliang, C. Guang, Z. Peitao, Z. Weide, M. Jinghao, W. Jingao, and C. Tae Eun · 2020
Closest in time.