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We propose a novel end-to-end deep scene flow model, called PointPWC-Net, on 3D point clouds in a coarse-to-fine fashion.
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Nguyen, T.M., Wu, Q.J.: Multiple kernel point set registration. IEEE transactions on medical imaging 35
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Danelljan, M., Meneghetti, G., Shahbaz Khan, F., Felsberg, M.: A probabilistic framework for color-based point set registration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1818–1826 (2016)
2016
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Dewan, A., Caselitz, T., Tipaldi, G.D., Burgard, W.: Rigid scene flow for 3d lidar scans. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). pp. 1765–1770. IEEE (2016)
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2018
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Li, Y., Bu, R., Sun, M., Wu, W., Di, X., Chen, B.: Pointcnn: Convolution on x-transformed points. In: Advances in Neural Information Processing Systems. pp. 820–830 (2018)
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Su, H., Jampani, V., Sun, D., Maji, S., Kalogerakis, E., Yang, M.H., Kautz, J.: Splatnet: Sparse lattice networks for point cloud processing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2530–2539 (2018)
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Sun, D., Yang, X., Liu, M.Y., Kautz, J.: Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 8934–8943 (2018)
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Tatarchenko, M., Park, J., Koltun, V., Zhou, Q.Y.: Tangent convolutions for dense prediction in 3d. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 3887–3896 (2018)
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Ushani, A.K., Eustice, R.M.: Feature learning for scene flow estimation from lidar. In: Conference on Robot Learning. pp. 283–292 (2018)
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Verma, N., Boyer, E., Verbeek, J.: Feastnet: Feature-steered graph convolutions for 3d shape analysis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2598–2606 (2018)
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Yin, Z., Shi, J.: Geonet: Unsupervised learning of dense depth, optical flow and camera pose. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1983–1992 (2018)
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Behl, A., Paschalidou, D., Donné, S., Geiger, A.: Pointflownet: Learning representations for rigid motion estimation from point clouds. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 7962–7971 (2019)
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Chabra, R., Straub, J., Sweeney, C., Newcombe, R., Fuchs, H.: Stereodrnet: Dilated residual stereonet. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 11786–11795 (2019)
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Gu, X., Wang, Y., Wu, C., Lee, Y.J., Wang, P.: Hplflownet: Hierarchical permutohedral lattice flownet for scene flow estimation on large-scale point clouds. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 3254–3263 (2019)
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Liu, L., Zhai, G., Ye, W., Liu, Y.: Unsupervised learning of scene flow estimation fusing with local rigidity. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence. pp. 876–882. AAAI Press (2019)
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Liu, X., Qi, C.R., Guibas, L.J.: Flownet3d: Learning scene flow in 3d point clouds. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 529–537 (2019)
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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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