Fetching the paper…
Reading the bibliography…
Scene flow estimation determines a scene's 3D motion field, by predicting the motion of points in the scene, especially for aiding tasks in autonomous driving.
S. Vedula, P. Rander, R. Collins, and T. Kanade, “Three-dimensional scene flow,”
2005
Earlier work this paper cites.
2015
Earlier work this paper cites.
M. Menze and A. Geiger, “Object scene flow for autonomous vehicles,” in
2015
Earlier work this paper cites.
A. Dosovitskiy, P. Fischer, E. Ilg, P. Hausser, C. Hazirbas, V. Golkov, P. Van Der Smagt, D. Cremers, and T. Brox, “Flownet: Learning optical flow with convolutional networks,” in
2015
Earlier work this paper cites.
F. Yu and V. Koltun, “Multi-scale context aggregation by dilated convolutions,”
2015
Earlier work this paper cites.
N. Mayer, E. Ilg, P. Hausser, P. Fischer, D. Cremers, A. Dosovitskiy, and T. Brox, “A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation,” in
2016
Earlier work this paper cites.
C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in
2017
Earlier work this paper cites.
D. Sun, X. Yang, M.-Y. Liu, and J. Kautz, “Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume,” in
2018
Earlier work this paper cites.
A. H. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang, and O. Beijbom, “Pointpillars: Fast encoders for object detection from point clouds,” in
2019
Earlier work this paper cites.
J. Hur and S. Roth, “Iterative residual refinement for joint optical flow and occlusion estimation,” in
2019
Earlier work this paper cites.
Y. Zhou, P. Sun, Y. Zhang, D. Anguelov, J. Gao, T. Ouyang, J. Guo, J. Ngiam, and V. Vasudevan, “End-to-end multi-view fusion for 3d object detection in lidar point clouds,” in
2020
Earlier work this paper cites.
Z. Teed and J. Deng, “Raft: Recurrent all-pairs field transforms for optical flow,” in
2020
Earlier work this paper cites.
P. Sun, H. Kretzschmar, X. Dotiwalla, A. Chouard, V. Patnaik, P. Tsui, J. Guo, Y. Zhou, Y. Chai, B. Caine
2020
Cited alongside, same era.
C. Luo, X. Yang, and A. Yuille, “Self-supervised pillar motion learning for autonomous driving,” in
2021
Cited alongside, same era.
Y. Wei, Z. Wang, Y. Rao, J. Lu, and J. Zhou, “PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of Point Clouds,” in
2021
Cited alongside, same era.
P. Jund, C. Sweeney, N. Abdo, Z. Chen, and J. Shlens, “Scalable scene flow from point clouds in the real world,”
2021
Cited alongside, same era.
B. Wilson, W. Qi, T. Agarwal, J. Lambert, J. Singh, S. Khandelwal, B. Pan, R. Kumar, A. Hartnett, J. K. Pontes, D. Ramanan, P. Carr, and J. Hays, “Argoverse 2: Next generation datasets for self-driving perception and forecasting,” in
2021
Cited alongside, same era.
Y. Hou, X. Zhu, Y. Ma, C. C. Loy, and Y. Li, “Point-to-voxel knowledge distillation for lidar semantic segmentation,” in
2022
Later among the works it cites.
J. Cen, P. Yun, S. Zhang, J. Cai, D. Luan, M. Tang, M. Liu, and M. Yu Wang, “Open-world semantic segmentation for lidar point clouds,” in
2022
Later among the works it cites.
S. Lee, H. Lim, and H. Myung, “Patchwork++: Fast and robust ground segmentation solving partial under-segmentation using 3D point cloud,” in
2022
Later among the works it cites.
2023
Later among the works it cites.
I. Lang, D. Aiger, F. Cole, S. Avidan, and M. Rubinstein, “Scoop: Self-supervised correspondence and optimization-based scene flow,” in
2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
X. Li, J. Kaesemodel Pontes, and S. Lucey, “Neural scene flow prior,”
2021
Cited alongside, same era.
F. Zhang, O. J. Woodford, V. A. Prisacariu, and P. H. Torr, “Separable flow: Learning motion cost volumes for optical flow estimation,” in
2021
Cited alongside, same era.
M. Najibi, J. Ji, Y. Zhou, C. R. Qi, X. Yan, S. Ettinger, and D. Anguelov, “Motion inspired unsupervised perception and prediction in autonomous driving,” in
2022
Cited alongside, same era.
2022
Cited alongside, same era.
W. Cheng and J. H. Ko, “Bi-pointflownet: Bidirectional learning for point cloud based scene flow estimation,” in
2022
Cited alongside, same era.
H. Xu, J. Zhang, J. Cai, H. Rezatofighi, and D. Tao, “Gmflow: Learning optical flow via global matching,” in
2022
Cited alongside, same era.
X. Sui, S. Li, X. Geng, Y. Wu, X. Xu, Y. Liu, R. Goh, and H. Zhu, “Craft: Cross-attentional flow transformer for robust optical flow,” in
2022
Cited alongside, same era.
Later among the works it cites.
Z. Wang, Y. Wei, Y. Rao, J. Zhou, and J. Lu, “3d point-voxel correlation fields for scene flow estimation,”
2023
Later among the works it cites.
N. Chodosh, D. Ramanan, and S. Lucey, “Re-evaluating lidar scene flow for autonomous driving,”
2023
Later among the works it cites.
2023
Later among the works it cites.
Z. Wang, Y. Wei, Y. Rao, J. Zhou, and J. Lu, “3d point-voxel correlation fields for scene flow estimation,”
2023
Later among the works it cites.
X. Li, J. Zheng, F. Ferroni, J. K. Pontes, and S. Lucey, “Fast neural scene flow,”
2023
Later among the works it cites.
A. 2, “Argoverse 2 scene flow online leaderboard,”
2023
Later among the works it cites.
2023
Later among the works it cites.