What Really Matters for Robust Multi-Sensor HD Map Construction?

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.

Y. Yan, Y. Mao, and B. Li, “SECOND: sparsely embedded convolutional detection,” Sensors , p. 3337, 2018

2018

Earlier work this paper cites.

C. Shorten and T. M. Khoshgoftaar, “A survey on image data augmentation for deep learning,” Journal of big data , vol. 6, no. 1, pp. 1–48, 2019

2019

Earlier work this paper cites.

H. Caesar, V. Bankiti et al. , “nuscenes: A multimodal dataset for autonomous driving,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 11 618–11 628

2020

Earlier work this paper cites.

J. Choi, Y. Song, and N. Kwak, “Part-aware data augmentation for 3d object detection in point cloud,” in IEEE/RSJ International Conference on Intelligent Robots and Systems , 2021, pp. 3391–3397

2021

Earlier work this paper cites.

T. Liang, H. Xie, K. Yu, Z. Xia, Z. Lin, Y. Wang, T. Tang, B. Wang, and Z. Tang, “Bevfusion: A simple and robust lidar-camera fusion framework,” 2022, pp. 10 421–10 434

2022

Earlier work this paper cites.

X. Wang, C. Fu, Z. Li, Y. Lai, and J. He, “Deepfusionmot: A 3d multi-object tracking framework based on camera-lidar fusion with deep association,” IEEE Robotics and Automation Letters , pp. 8260–8267, 2022

2022

Earlier work this paper cites.

Q. Li, Y. Wang, Y. Wang, and H. Zhao, “Hdmapnet: An online hd map construction and evaluation framework,” in International Conference on Robotics and Automation (ICRA) , 2022, pp. 4628–4634

2022

Earlier work this paper cites.

S. Chen, T. Cheng, X. Wang, W. Meng, Q. Zhang, and W. Liu, “Efficient and robust 2d-to-bev representation learning via geometry-guided kernel transformer,” arXiv preprint arXiv:2206.04584 , 2022

Original

2022

Earlier work this paper cites.

B. Liao, S. Chen, X. Wang, T. Cheng, Q. Zhang, W. Liu, and C. Huang, “Maptr: Structured modeling and learning for online vectorized hd map construction,” in International Conference on Learning Representations , 2023

2023

Earlier work this paper cites.

S. Xie, L. Kong, W. Zhang, J. Ren, L. Pan, K. Chen, and Z. Liu, “Robobev: Towards robust bird’s eye view perception under corruptions,” arXiv preprint arXiv:2304.06719 , 2023

Original

2023

Earlier work this paper cites.

L. Kong, Y. Liu, X. Li, R. Chen, W. Zhang, J. Ren, L. Pan, K. Chen, and Z. Liu, “Robo3d: Towards robust and reliable 3d perception against corruptions,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 19 994–20 006

2023

Earlier work this paper cites.

Z. Zhu, Y. Zhang, H. Chen, Y. Dong, S. Zhao, W. Ding, J. Zhong, and S. Zheng, “Understanding the robustness of 3d object detection with bird’s-eye-view representations in autonomous driving,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2023, pp. 21 600–21 610

2023

Earlier work this paper 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 , 2023, pp. 22 352–22 369

2023

Cited alongside, same era.

W. Ding, L. Qiao, X. Qiu, and C. Zhang, “Pivotnet: Vectorized pivot learning for end-to-end hd map construction,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 3672–3682

2023

Cited alongside, same era.

L. Qiao, W. Ding, X. Qiu, and C. Zhang, “End-to-end vectorized hd-map construction with piecewise bezier curve,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 13 218–13 228

2023

Cited alongside, same era.

Z. Liu, H. Tang, A. Amini, X. Yang, H. Mao, D. L. Rus, and S. Han, “Bevfusion: Multi-task multi-sensor fusion with unified bird’s-eye view representation,” in IEEE international conference on robotics and automation (ICRA) , 2023, pp. 2774–2781

2023

Cited alongside, same era.

B. Liao, S. Chen, Y. Zhang, B. Jiang, Q. Zhang, W. Liu, C. Huang, and X. Wang, “Maptrv2: An end-to-end framework for online vectorized hd map construction,” International Journal of Computer Vision , pp. 1–23, 2024

2024

Later among the works it cites.

T. Yuan, Y. Liu, Y. Wang, Y. Wang, and H. Zhao, “Streammapnet: Streaming mapping network for vectorized online hd map construction,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2024, pp. 7356–7365

2024

Later among the works it cites.

Y. Zhou, H. Zhang, J. Yu, Y. Yang, S. Jung, S.-I. Park, and B. Yoo, “Himap: Hybrid representation learning for end-to-end vectorized hd map construction,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 15 396–15 406

2024

Later among the works it cites.

X. Hao, H. Zhang, Y. Yang, Y. Zhou, S. Jung, S.-I. Park, and B. Yoo, “Mbfusion: A new multi-modal bev feature fusion method for hd map construction,” in IEEE International Conference on Robotics and Automation (ICRA) , 2024, pp. 15 922–15 928