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Methods for 3D lane detection have been recently proposed to address the issue of inaccurate lane layouts in many autonomous driving scenarios (uphill/downhill, bump, etc.).
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Aly, M.: Real time detection of lane markers in urban streets. In: IV (2008)
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Benmansour, N., Labayrade, R., Aubert, D., Glaser, S.: Stereovision-based 3d lane detection system: a model driven approach. In: ITSC (2008)
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Bai, M., Mattyus, G., Homayounfar, N., Wang, S., Lakshmikanth, S.K., Urtasun, R.: Deep multi-sensor lane detection. In: IROS (2018)
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Meyer, A., Salscheider, N.O., Orzechowski, P.F., Stiller, C.: Deep semantic lane segmentation for mapless driving. In: IROS (2018)
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Neven, D., De Brabandere, B., Georgoulis, S., Proesmans, M., Van Gool, L.: Towards end-to-end lane detection: an instance segmentation approach. In: IV (2018)
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Pan, X., Shi, J., Luo, P., Wang, X., Tang, X.: Spatial as deep: Spatial cnn for traffic scene understanding. In: AAAI (2018)
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Behrendt, K., Soussan, R.: Unsupervised labeled lane markers using maps. In: ICCV (2019)
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Chang, M.F., Lambert, J., Sangkloy, P., Singh, J., Bak, S., Hartnett, A., Wang, D., Carr, P., Lucey, S., Ramanan, D., et al.: Argoverse: 3d tracking and forecasting with rich maps. In: CVPR (2019)
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Chen, Z., Liu, Q., Lian, C.: Pointlanenet: Efficient end-to-end cnns for accurate real-time lane detection. In: IV (2019)
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Garnett, N., Cohen, R., Pe’er, T., Lahav, R., Levi, D.: 3d-lanenet: End-to-end 3d multiple lane detection. In: ICCV (2019)
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Hou, Y., Ma, Z., Liu, C., Loy, C.C.: Learning lightweight lane detection cnns by self attention distillation. In: ICCV (2019)
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Huang, X., Wang, P., Cheng, X., Zhou, D., Geng, Q., Yang, R.: The apolloscape open dataset for autonomous driving and its application. TPAMI (2019)
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Li, X., Li, J., Hu, X., Yang, J.: Line-cnn: End-to-end traffic line detection with line proposal unit. T-ITS (2019)
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Liang, M., Yang, B., Chen, Y., Hu, R., Urtasun, R.: Multi-task multi-sensor fusion for 3d object detection. In: CVPR (2019)
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Tan, M., Le, Q.: Efficientnet: Rethinking model scaling for convolutional neural networks. In: ICML (2019)
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Jin, Y., Ren, X., Chen, F., Zhang, W.: Robust monocular 3d lane detection with dual attention. In: ICIP (2021)
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Kumar, V.R., Yogamani, S., Rashed, H., Sitsu, G., Witt, C., Leang, I., Milz, S., Mäder, P.: Omnidet: Surround view cameras based multi-task visual perception network for autonomous driving. RA-L (2021)
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Liu, L., Chen, X., Zhu, S., Tan, P.: Condlanenet: a top-to-down lane detection framework based on conditional convolution. In: CVPR (2021)
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Caesar, H., Bankiti, V., Lang, A.H., Vora, S., Liong, V.E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., Beijbom, O.: nuscenes: A multimodal dataset for autonomous driving. In: CVPR (2020)
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Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: ECCV (2020)
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Gao, J., Sun, C., Zhao, H., Shen, Y., Anguelov, D., Li, C., Schmid, C.: Vectornet: Encoding hd maps and agent dynamics from vectorized representation. In: CVPR (2020)
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Garnett, N., Uziel, R., Efrat, N., Levi, D.: Synthetic-to-real domain adaptation for lane detection. In: ACCV (2020)
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Guo, Y., Chen, G., Zhao, P., Zhang, W., Miao, J., Wang, J., Choe, T.E.: Gen-lanenet: A generalized and scalable approach for 3d lane detection. In: ECCV (2020)
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Qin, Z., Wang, H., Li, X.: Ultra fast structure-aware deep lane detection. In: ECCV (2020)
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Shan, T., Englot, B., Meyers, D., Wang, W., Ratti, C., Daniela, R.: Lio-sam: Tightly-coupled lidar inertial odometry via smoothing and mapping. In: IROS (2020)
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Prakash, A., Chitta, K., Geiger, A.: Multi-modal fusion transformer for end-to-end autonomous driving. In: CVPR (2021)
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Shan, T., Englot, B., Ratti, C., Daniela, R.: Lvi-sam: Tightly-coupled lidar-visual-inertial odometry via smoothing and mapping. In: ICRA (2021)
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Su, J., Chen, C., Zhang, K., Luo, J., Wei, X., Wei, X.: Structure guided lane detection. In: IJCAI-21 (2021)
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Tabelini, L., Berriel, R., Paixao, T.M., Badue, C., De Souza, A.F., Oliveira-Santos, T.: Keep your eyes on the lane: Real-time attention-guided lane detection. In: CVPR (2021)
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Yang, W., Li, Q., Liu, W., Yu, Y., Ma, Y., He, S., Pan, J.: Projecting your view attentively: Monocular road scene layout estimation via cross-view transformation. In: CVPR (2021)
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Zhang, Y., Zhu, L., Feng, W., Fu, H., Wang, M., Li, Q., Li, C., Wang, S.: Vil-100: A new dataset and a baseline model for video instance lane detection. In: ICCV (2021)
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Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable DETR: Deformable transformers for end-to-end object detection. In: ICLR (2021)
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Chen, X., Liao, W., Liu, B., Yan, J., He, T.: Opendenselane: a new lidar-based dataset for hd map construction. In: ICME (2022)
2022
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Guan, T., Wang, J., Lan, S., Chandra, R., Wu, Z., Davis, L., Manocha, D.: M3detr: Multi-representation, multi-scale, mutual-relation 3d object detection with transformers. In: WACV (2022)
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Liu, R., Chen, D., Liu, T., Xiong, Z., Yuan, Z.: Learning to predict 3d lane shape and camera pose from a single image via geometry constraints. In: AAAI (2022)
2022
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Saha, A., Maldonado, O.M., Russell, C., Bowden, R.: Translating images into maps. In: ICRA (2022)
2022
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Wang, Y., Guizilini, V.C., Zhang, T., Wang, Y., Zhao, H., Solomon, J.: Detr3d: 3d object detection from multi-view images via 3d-to-2d queries. In: CoRL (2022)
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Yan, F., Nie, M., Cai, X., Han, J., Xu, H., Yang, Z., Ye, C., Fu, Y., Mi, M.B., Zhang, L.: Once-3dlanes: Building monocular 3d lane detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 17143–17152 (June 2022)
2022
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Yan, F., Nie, M., Cai, X., Han, J., Xu, H., Yang, Z., Ye, C., Fu, Y., Michael, B.M., Zhang, L.: Once-3dlanes: Building monocular 3d lane detection. In: CVPR (2022)
2022
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