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3D multi-object tracking (MOT) is essential to applications such as autonomous driving.
W Kuhn, H.: The Hungarian Method for the Assignment Problem. Naval Research Logistics Quarterly (1955)
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Kalman, R.: A New Approach to Linear Filtering and Prediction Problems. Journal of Basic Engineering (1960)
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Geiger, A., Lenz, P., Urtasun, R.: Are We Ready for Autonomous Driving? the KITTI Vision Benchmark Suite. CVPR (2012)
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Wojke, N., Bewley, A., Paulus, D.: Simple Online and Realtime Tracking with a Deep Association Metric. ICIP (2017)
2017
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2018
Cited alongside, same era.
Shi, S., Wang, X., Li, H.: PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud. CVPR (2019)
2019
Cited alongside, same era.
Zhang, W., Zhou, H., Sun, S., Wang, Z., Shi, J., Loy, C.C.: Robust Multi-Modality Multi-Object Tracking. ICCV (2019)
2019
Cited alongside, same era.
Baser, E., Balasubramanian, V., Bhattacharyya, P., Czarnecki, K.: FANTrack: 3D Multi-Object Tracking with Feature Association Network. IV (2020)
2020
Closest in time.
Weng, X., Wang, Y., Man, Y., Kitani, K.: GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking with 2D-3D Multi-Feature Learning. CVPR (2020)
2020
Closest in time.
2020
Closest in time.
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