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The purpose of this work is to review the state-of-the-art LiDAR-based 3D object detection methods, datasets, and challenges.
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M. Sanatkar, “Lidar 3d Object Detection Methods,” https://towardsdatascience.com/lidar-3d-object-detection-methods-f34cf3227aea, Jun. 2020
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Y. Wu, Y. Wang, S. Zhang, and H. Ogai, “Deep 3D Object Detection Networks Using LiDAR Data: A Review,” IEEE Sensors Journal , vol. 21, no. 2, pp. 1152–1171, Jan. 2021
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S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang, and H. Li, “PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection,” 2020, pp. 10 529–10 538
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Later among the works it cites.
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Later among the works it cites.
2021
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E. Erçelik, E. Yurtsever, and A. Knoll, “Temp-frustum net: 3d object detection with temporal fusion,” 2021
2021
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Z. Yang, Y. Zhou, Z. Chen, and J. Ngiam, “3d-man: 3d multi-frame attention network for object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 1863–1872
2021
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C. R. Qi, Y. Zhou, M. Najibi, P. Sun, K. Vo, B. Deng, and D. Anguelov, “Offboard 3d object detection from point cloud sequences,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 6134–6144
2021
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Z. Xiong, H. Ma, Y. Wang, T. Hu, and Q. Liao, “Lidar-based 3d video object detection with foreground context modeling and spatiotemporal graph reasoning,” in 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) . IEEE, 2021, pp. 2994–3001
2021
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J. Yin, J. Shen, X. Gao, D. Crandall, and R. Yang, “Graph neural network and spatiotemporal transformer attention for 3d video object detection from point clouds,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2021
2021
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Z. Yuan, X. Song, L. Bai, Z. Wang, and W. Ouyang, “Temporal-channel transformer for 3d lidar-based video object detection for autonomous driving,” IEEE Transactions on Circuits and Systems for Video Technology , 2021
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2022
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