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3D object detection models that exploit both LiDAR and camera sensor features are top performers in large-scale autonomous driving benchmarks.
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2022
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2020
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MMDetection3D Contributors, “OpenMMLab’s Next-generation Platform for General 3D Object Detection,” July 2020
2020
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X. Zhu, W. Su, L. Lu, B. Li, X. Wang, and J. Dai, “Deformable DETR: Deformable Transformers for End-to-End Object Detection,” in International Conference on Learning Representations (ICLR) , 2021
2021
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2021
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I. Misra, R. Girdhar, and A. Joulin, “An End-to-End Transformer Model for 3D Object Detection,” in 2021 IEEE/CVF International Conference on Computer Vision (ICCV) . Montreal, QC, Canada: IEEE, Oct. 2021, pp. 2886–2897
2021
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L. Fan, X. Xiong, F. Wang, N. Wang, and Z. Zhang, “RangeDet: In Defense of Range View for LiDAR-based 3D Object Detection,” in 2021 IEEE/CVF International Conference on Computer Vision (ICCV) . Montreal, QC, Canada: IEEE, Oct. 2021, pp. 2898–2907
2021
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Y. Chai, P. Sun, J. Ngiam, W. Wang, B. Caine, V. Vasudevan, X. Zhang, and D. Anguelov, “To the Point: Efficient 3D Object Detection in the Range Image with Graph Convolution Kernels,” in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Nashville, TN, USA: IEEE, June 2021, pp. 15 995–16 004
2021
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P. Sun, W. Wang, Y. Chai, G. Elsayed, A. Bewley, X. Zhang, C. Sminchisescu, and D. Anguelov, “RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detection,” in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Nashville, TN, USA: IEEE, June 2021, pp. 5721–5730
2021
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Z. Li, F. Wang, and N. Wang, “LiDAR R-CNN: An Efficient and Universal 3D Object Detector,” in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Nashville, TN, USA: IEEE, June 2021, pp. 7542–7551
2021
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F. Drews, D. Feng, F. Faion, L. Rosenbaum, M. Ulrich, and C. Gläser, “DeepFusion: A Robust and Modular 3D Object Detector for Lidars, Cameras and Radars,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2022, pp. 560–567
2022
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Y. Zeng, D. Zhang, C. Wang, Z. Miao, T. Liu, X. Zhan, D. Hao, and C. Ma, “LIFT: Learning 4D LiDAR Image Fusion Transformer for 3D Object Detection,” in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . New Orleans, LA, USA: IEEE, June 2022, pp. 17 151–17 160
2022
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2022
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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,” in Advances in Neural Information Processing Systems , S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, Eds., vol. 35. Curran Associates, Inc., 2022, pp. 10 421–10 434
2022
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2022
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2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
X. Chen, T. Zhang, Y. Wang, Y. Wang, and H. Zhao, “FUTR3D: A Unified Sensor Fusion Framework for 3D Detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops , 2023, pp. 172–181
2023
Closest in time.
J. Mao, S. Shi, X. Wang, and H. Li, “3D Object Detection for Autonomous Driving: A Comprehensive Survey,” International Journal of Computer Vision , vol. 131, no. 8, pp. 1909–1963, Aug. 2023
2023
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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 2023 IEEE International Conference on Robotics and Automation (ICRA) , 2023, pp. 2774–2781
2023
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P. Jacobson, Y. Zhou, W. Zhan, M. Tomizuka, and M. C. Wu, “Center Feature Fusion: Selective Multi-Sensor Fusion of Center-based Objects,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) , 2023, pp. 8312–8318
2023
Closest in time.