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Multi-modal object detection in autonomous driving has achieved great breakthroughs due to the usage of fusing complementary information from different sensors.
X. Chen, H. Ma, J. Wan, B. Li, and T. Xia, “Multi-view 3d object detection network for autonomous driving,” in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition , 2017, pp. 1907–1915
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Z. Taylor and J. Nieto, “Motion-based calibration of multimodal sensor extrinsics and timing offset estimation,” IEEE Transactions on Robotics , vol. 32, no. 5, pp. 1215–1229, 2016
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N. Schneider, F. Piewak, C. Stiller, and U. Franke, “Regnet: Multimodal sensor registration using deep neural networks,” in 2017 IEEE intelligent vehicles symposium (IV) . IEEE, 2017, pp. 1803–1810
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2017
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D. Xu, D. Anguelov, and A. Jain, “Pointfusion: Deep sensor fusion for 3d bounding box estimation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 244–253
2018
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C. R. Qi, W. Liu, C. Wu, H. Su, and L. J. Guibas, “Frustum pointnets for 3d object detection from rgb-d data,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 918–927
2018
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2018
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J. Ku, A. D. Pon, and S. L. Waslander, “Monocular 3d object detection leveraging accurate proposals and shape reconstruction,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 11 867–11 876
2019
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S. Shi, X. Wang, and H. Li, “Pointrcnn: 3d object proposal generation and detection from point cloud,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 770–779
2019
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2019
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X. Zhao, Z. Liu, R. Hu, and K. Huang, “3d object detection using scale invariant and feature reweighting networks,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, no. 01, 2019, pp. 9267–9274
2019
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T. Huang, Z. Liu, X. Chen, and X. Bai, “Epnet: Enhancing point features with image semantics for 3d object detection,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XV 16 . Springer, 2020, pp. 35–52
2020
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S. Vora, A. H. Lang, B. Helou, and O. Beijbom, “Pointpainting: Sequential fusion for 3d object detection,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 4604–4612
2020
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S. Pang, D. Morris, and H. Radha, “Clocs: Camera-lidar object candidates fusion for 3d object detection,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 10 386–10 393
2020
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K. Yuan, Z. Guo, and Z. J. Wang, “Rggnet: Tolerance aware lidar-camera online calibration with geometric deep learning and generative model,” IEEE Robotics and Automation Letters , vol. 5, no. 4, pp. 6956–6963, 2020
2020
Cited alongside, same era.
C. Park, P. Moghadam, S. Kim, S. Sridharan, and C. Fookes, “Spatiotemporal camera-lidar calibration: A targetless and structureless approach,” IEEE Robotics and Automation Letters , vol. 5, no. 2, pp. 1556–1563, 2020
2020
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Z. Liu, D. Zhou, F. Lu, J. Fang, and L. Zhang, “Autoshape: Real-time shape-aware monocular 3d object detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 15 641–15 650
2021
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T. Yin, X. Zhou, and P. Krahenbuhl, “Center-based 3d object detection and tracking,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021, pp. 11 784–11 793
2021
Cited alongside, same era.
P. Rotter, M. Klemiato, and P. Skruch, “Automatic calibration of a lidar–camera system based on instance segmentation,” Remote Sensing , vol. 14, no. 11, p. 2531, 2022
2022
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A. Kodaira, Y. Zhou, P. Zang, W. Zhan, and M. Tomizuka, “Sst-calib: Simultaneous spatial-temporal parameter calibration between lidar and camera,” in 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) . IEEE, 2022, pp. 2896–2902
2022
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Y. Hou, X. Zhu, Y. Ma, C. C. Loy, and Y. Li, “Point-to-voxel knowledge distillation for lidar semantic segmentation,” in IEEE Conference on Computer Vision and Pattern Recognition , 2022, pp. 8479–8488
2022
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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) . IEEE, 2023, pp. 2774–2781
2023
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C. Wang, C. Ma, M. Zhu, and X. Yang, “Pointaugmenting: Cross-modal augmentation for 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 11 794–11 803
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Z. Liu, H. Tang, S. Zhu, and S. Han, “Semalign: Annotation-free camera-lidar calibration with semantic alignment loss,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 8845–8851
2021
Cited alongside, same era.
S. Xu, D. Zhou, J. Fang, J. Yin, Z. Bin, and L. Zhang, “Fusionpainting: Multimodal fusion with adaptive attention for 3d object detection,” in 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) . IEEE, 2021, pp. 3047–3054
2021
Cited alongside, same era.
X. Lv, B. Wang, Z. Dou, D. Ye, and S. Wang, “Lccnet: Lidar and camera self-calibration using cost volume network,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 2894–2901
2021
Cited alongside, same era.
J. Peršić, L. Petrović, I. Marković, and I. Petrović, “Spatiotemporal multisensor calibration via gaussian processes moving target tracking,” Ieee transactions on robotics , vol. 37, no. 5, pp. 1401–1415, 2021
2021
Cited alongside, same era.
X. Zhu, H. Zhou, T. Wang, F. Hong, Y. Ma, W. Li, H. Li, and D. Lin, “Cylindrical and asymmetrical 3d convolution networks for lidar segmentation,” in IEEE Conference on Computer Vision and Pattern Recognition , 2021, pp. 9939–9948
2021
Cited alongside, same era.
X. Zhu, H. Zhou, T. Wang, F. Hong, W. Li, Y. Ma, H. Li, R. Yang, and D. Lin, “Cylindrical and asymmetrical 3d convolution networks for lidar-based perception,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2021
2021
Cited alongside, same era.
Later among the works it cites.
K. Wang, Y. Wang, B. Liu, and J. Chen, “Quantification of uncertainty and its applications to complex domain for autonomous vehicles perception system,” IEEE Transactions on Instrumentation and Measurement , vol. 72, pp. 1–17, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
Y. Dong, C. Kang, J. Zhang, Z. Zhu, Y. Wang, X. Yang, H. Su, X. Wei, and J. Zhu, “Benchmarking robustness of 3d object detection to common corruptions,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 1022–1032
2023
Later among the works it cites.
K. Yu, T. Tao, H. Xie, Z. Lin, T. Liang, B. Wang, P. Chen, D. Hao, Y. Wang, and X. Liang, “Benchmarking the robustness of lidar-camera fusion for 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 3188–3198
2023
Later among the works it cites.
2023
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 , 2023, pp. 172–181
2023
Later among the works it cites.
A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo et al. , “Segment anything,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 4015–4026
2023
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2023
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Z. Song, C. Jia, L. Yang, H. Wei, and L. Liu, “Graphalign++: An accurate feature alignment by graph matching for multi-modal 3d object detection,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 34, no. 4, pp. 2619–2632, 2024
2024
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2024
Closest in time.
J. Dong, D. Yao, Y. Hu, S. Zhou, and N. Zheng, “A novel dense object detector with scale balanced sample assignment and refinement,” IEEE Transactions on Circuits and Systems for Video Technology , 2025
2025
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