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The construction of online vectorized High-Definition (HD) maps is critical for downstream prediction and planning.
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Ding, W., Qiao, L., Qiu, X., Zhang, C.: Pivotnet: Vectorized pivot learning for end-to-end hd map construction. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 3672–3682 (2023)
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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: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 294–302 (2021)
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Tabelini, L., Berriel, R., Paixao, T.M., Badue, C., De Souza, A.F., Oliveira-Santos, T.: Polylanenet: Lane estimation via deep polynomial regression. In: 2020 25th International Conference on Pattern Recognition (ICPR). pp. 6150–6156. IEEE (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: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 15536–15545 (2021)
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Deo, N., Wolff, E., Beijbom, O.: Multimodal trajectory prediction conditioned on lane-graph traversals. In: Conference on Robot Learning. pp. 203–212. PMLR (2022)
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Feng, Z., Guo, S., Tan, X., Xu, K., Wang, M., Ma, L.: Rethinking efficient lane detection via curve modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 17062–17070 (2022)
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Li, C., Shi, J., Wang, Y., Cheng, G.: Reconstruct from top view: A 3d lane detection approach based on geometry structure prior. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4370–4379 (2022)
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Gu, J., Hu, C., Zhang, T., Chen, X., Wang, Y., Wang, Y., Zhao, H.: Vip3d: End-to-end visual trajectory prediction via 3d agent queries. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5496–5506 (2023)
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Li, Y., Ge, Z., Yu, G., Yang, J., Wang, Z., Shi, Y., Sun, J., Li, Z.: Bevdepth: Acquisition of reliable depth for multi-view 3d object detection. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 37, pp. 1477–1485 (2023)
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Liao, B., Chen, S., Wang, X., Cheng, T., Zhang, Q., Liu, W., Huang, C.: MapTR: Structured modeling and learning for online vectorized HD map construction. In: The Eleventh International Conference on Learning Representations (2023), https://openreview.net/forum?id=k7p_YAO7yE
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2023
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Liu, Y., Yuan, T., Wang, Y., Wang, Y., Zhao, H.: Vectormapnet: End-to-end vectorized hd map learning. In: International Conference on Machine Learning. pp. 22352–22369. PMLR (2023)
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Liu, Z., Tang, H., Amini, A., Yang, X., Mao, H., Rus, D.L., Han, S.: Bevfusion: Multi-task multi-sensor fusion with unified bird’s-eye view representation. In: 2023 IEEE International Conference on Robotics and Automation (ICRA). pp. 2774–2781. IEEE (2023)
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Qiao, L., Ding, W., Qiu, X., Zhang, C.: End-to-end vectorized hd-map construction with piecewise bezier curve. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 13218–13228 (2023)
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2023
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Xie, Z., Pang, Z., Wang, Y.X.: Mv-map: Offboard hd-map generation with multi-view consistency. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 8658–8668 (2023)
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Zhang, G., Lin, J., Wu, S., Luo, Z., Xue, Y., Lu, S., Wang, Z., et al.: Online map vectorization for autonomous driving: A rasterization perspective. Advances in Neural Information Processing Systems 36
2024
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