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Scene flow estimation predicts the 3D motion at each point in successive LiDAR scans.
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Aleotti, F., Poggi, M., Tosi, F., Mattoccia, S.: Learning end-to-end scene flow by distilling single tasks knowledge. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 34, pp. 10435–10442 (2020)
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Mittal, H., Okorn, B., Held, D.: Just go with the flow: Self-supervised scene flow estimation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 11177–11185 (2020)
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Sun, P., Kretzschmar, H., Dotiwalla, X., Chouard, A., Patnaik, V., Tsui, P., Guo, J., Zhou, Y., Chai, Y., Caine, B., et al.: Scalability in perception for autonomous driving: Waymo open dataset. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 2446–2454 (2020)
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Tishchenko, I., Lombardi, S., Oswald, M.R., Pollefeys, M.: Self-supervised learning of non-rigid residual flow and ego-motion. In: 2020 international conference on 3D vision (3DV). pp. 150–159. IEEE (2020)
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Wu, W., Wang, Z.Y., Li, Z., Liu, W., Fuxin, L.: Pointpwc-net: Cost volume on point clouds for self-supervised scene flow estimation. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part V 16. pp. 88–107. Springer (2020)
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Baur, S.A., Emmerichs, D.J., Moosmann, F., Pinggera, P., Ommer, B., Geiger, A.: Slim: Self-supervised lidar scene flow and motion segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 13126–13136 (2021)
2021
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Jund, P., Sweeney, C., Abdo, N., Chen, Z., Shlens, J.: Scalable scene flow from point clouds in the real world. IEEE Robotics and Automation Letters
2021
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Kittenplon, Y., Eldar, Y.C., Raviv, D.: Flowstep3d: Model unrolling for self-supervised scene flow estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4114–4123 (2021)
2021
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Li, X., Kaesemodel Pontes, J., Lucey, S.: Neural scene flow prior. Advances in Neural Information Processing Systems
2021
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Song, J., Lee, S.J.: Knowledge distillation of multi-scale dense prediction transformer for self-supervised depth estimation. Scientific Reports (18939) (2023)
2023
Later among the works it cites.
Wang, Z., Wei, Y., Rao, Y., Zhou, J., Lu, J.: 3d point-voxel correlation fields for scene flow estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence (2023)
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Zhang, Q., Duberg, D., Geng, R., Jia, M., Wang, L., Jensfelt, P.: A dynamic points removal benchmark in point cloud maps. In: IEEE 26th International Conference on Intelligent Transportation Systems (ITSC). pp. 608–614 (2023)
2023
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2024
Closest in time.
Chodosh, N., Ramanan, D., Lucey, S.: Re-evaluating lidar scene flow. In: 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). pp. 5993–6003 (2024). https://doi.org/10.1109/WACV57701.2024.00590
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Pfreundschuh, P., Hendrikx, H.F., Reijgwart, V., Dubé, R., Siegwart, R., Cramariuc, A.: Dynamic object aware lidar slam based on automatic generation of training data. In: 2021 IEEE International Conference on Robotics and Automation (ICRA). pp. 11641–11647. IEEE (2021)
2021
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Wei, Y., Wang, Z., Rao, Y., Lu, J., Zhou, J.: PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of Point Clouds. In: CVPR (2021)
2021
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Wilson, B., Qi, W., Agarwal, T., Lambert, J., Singh, J., et al.: Argoverse 2: Next generation datasets for self-driving perception and forecasting. In: Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks (NeurIPS Datasets and Benchmarks 2021) (2021)
2021
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Najibi, M., Ji, J., Zhou, Y., Qi, C.R., Yan, X., Ettinger, S., Anguelov, D.: Motion inspired unsupervised perception and prediction in autonomous driving. In: European Conference on Computer Vision. pp. 424–443. Springer (2022)
2022
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Deng, D., Zakhor, A.: Rsf: Optimizing rigid scene flow from 3d point clouds without labels. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 1277–1286 (2023)
2023
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Lang, I., Aiger, D., Cole, F., Avidan, S., Rubinstein, M.: Scoop: Self-supervised correspondence and optimization-based scene flow. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5281–5290 (2023)
2023
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Li, X., Zheng, J., Ferroni, F., Pontes, J.K., Lucey, S.: Fast neural scene flow. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 9878–9890 (2023)
2023
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Schmid, L., Andersson, O., Sulser, A., Pfreundschuh, P., Siegwart, R.: Dynablox: Real-time detection of diverse dynamic objects in complex environments. IEEE Robotics and Automation Letters (RA-L)
2023
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2024
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Duberg, D., Zhang, Q., Jia, M., Jensfelt, P.: DUFOMap: Efficient dynamic awareness mapping. IEEE Robotics and Automation Letters
2024
Closest in time.
Liu, J., Wang, G., Ye, W., Jiang, C., Han, J., Liu, Z., Zhang, G., Du, D., Wang, H.: Difflow3d: Toward robust uncertainty-aware scene flow estimation with iterative diffusion-based refinement. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 15109–15119 (2024)
2024
Closest in time.
Vedder, K., Khatri, I., Peri, N., Chodosh, N., Liu, Y., Hays, J.: Av2 2024 scene flow challenge announcement
2024
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Vedder, K., Peri, N., Chodosh, N., Khatri, I., Eaton, E., Jayaraman, D., Ramanan, Y.L.D., Hays, J.: ZeroFlow: Fast Zero Label Scene Flow via Distillation. International Conference on Learning Representations (ICLR) (2024)
2024
Closest in time.
Vidanapathirana, K., Chng, S.F., Li, X., Lucey, S.: Multi-body neural scene flow. In: 2024 International Conference on 3D Vision (3DV). pp. 126–136. IEEE (2024)
2024
Closest in time.
Wu, H., Li, Y., Xu, W., Kong, F., Zhang, F.: Moving event detection from lidar point streams. Nature Communications
2024
Closest in time.
Zhang, Q., Yang, Y., Fang, H., Geng, R., Jensfelt, P.: DeFlow: Decoder of scene flow network in autonomous driving. In: 2024 IEEE International Conference on Robotics and Automation (ICRA). pp. 2105–2111 (2024). https://doi.org/10.1109/ICRA57147.2024.10610278
2024
Closest in time.