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Understanding the motion states of the surrounding environment is critical for safe autonomous driving.
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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
2021
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Y. Kittenplon, Y. C. Eldar, and D. Raviv, “Flowstep3d: Model unrolling for self-supervised scene flow estimation,” in
2021
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R. Li, G. Lin, T. He, F. Liu, and C. Shen, “Hcrf-flow: Scene flow from point clouds with continuous high-order crfs and position-aware flow embedding,” in
2021
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P. Jund, C. Sweeney, N. Abdo, Z. Chen, and J. Shlens, “Scalable scene flow from point clouds in the real world,”
2022
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R. Li, C. Zhang, G. Lin, Z. Wang, and C. Shen, “Rigidflow: Self-supervised scene flow learning on point clouds by local rigidity prior,” in
2022
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R. Battrawy, R. Schuster, M.-A. N. Mahani, and D. Stricker, “Rms-flownet: Efficient and robust multi-scale scene flow estimation for large-scale point clouds,” in
2022
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Q. Zhang, Y. Yang, H. Fang, R. Geng, and P. Jensfelt, “Deflow: Decoder of scene flow network in autonomous driving,” in
2024
Closest in time.
D. Liu, D. Liu, X. Li, S. Lin, B. Wang, X. Chang, L. Chu,
2024
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K. Vedder, N. Peri, N. E. Chodosh, I. Khatri, E. Eaton, D. Jayaraman, Y. Liu, D. Ramanan, and J. Hays, “Zeroflow: Scalable scene flow via distillation,” in
2024
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Y. Lin and H. Caesar, “Icp-flow: Lidar scene flow estimation with icp,” in
2024
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