[1] Aseem Behl, Omid Hosseini Jafari, Siva Karthik Mustikovela, Hassan Abu Alhaija, Carsten Rother, and Andreas Geiger. Bounding boxes, segmentations and object coordinates: How important is recognition for 3d scene flow estimation in autonomous driving scenarios? In Proceedings of the IEEE International Conference on Computer Vision, pages 2574-2583, 2017.
[2] Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al. Shapenet: An information-rich 3d model repository. arXiv preprint arXiv:1512.03012, 2015.
[3] Wencan Cheng and Jong Hwan Ko. Bi-pointflownet: Bidirectional learning for point cloud based scene flow estimation. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XXVIII, pages 108-124. Springer, 2022.
[4] Nathaniel Chodosh, Deva Ramanan, and Simon Lucey. Re-evaluating lidar scene flow for autonomous driving. arXiv preprint arXiv:2304.02150, 2023.
[5] Haowen Deng, Tolga Birdal, and Slobodan Ilic. Ppf-foldnet: Unsupervised learning of rotation invariant 3d local descriptors. In Proceedings of the European conference on computer vision (ECCV), pages 602-618, 2018.
[6] Haowen Deng, Tolga Birdal, and Slobodan Ilic. Ppfnet: Global context aware local features for robust 3d point matching. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 195-205, 2018.
[7] Lihe Ding, Shaocong Dong, Tingfa Xu, Xinli Xu, Jie Wang, and Jianan Li. Fh-net: A fast hierarchical network for scene flow estimation on real-world point clouds. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XXXIX, pages 213-229. Springer, 2022.
[8] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020.
[9] Alexey Dosovitskiy, Philipp Fischer, Eddy Ilg, Philip Hausser, Caner Hazirbas, Vladimir Golkov, Patrick Van Der Smagt, Daniel Cremers, and Thomas Brox. Flownet: Learning optical flow with convolutional networks. In Proceedings of the IEEE international conference on computer vision, pages 2758-2766, 2015.
[10] Johan Edstedt, Ioannis Athanasiadis, Mårten Wadenback, and Michael Felsberg. Dkm: Dense kernelized feature matching for geometry estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17765-17775, 2023.
[11] Jingyun Fu, Zhiyu Xiang, Chengyu Qiao, and Tingming Bai. Pt-flownet: Scene flow estimation on point clouds with point transformer. IEEE Robotics and Automation Letters, 8(5):2566-2573, 2023.
[12] Zan Gojcic, Caifa Zhou, Jan D Wegner, and Andreas Wieser. The perfect match: 3d point cloud matching with smoothed densities. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5545-5554, 2019.
[13] Xiaodong Gu, Chengzhou Tang, Weihao Yuan, Zuozhuo Dai, Siyu Zhu, and Ping Tan. Rcp: Recurrent closest point for scene flow estimation on 3d point clouds. arXiv preprint arXiv:2205.11028, 2022.
[14] Xiuye Gu, Yijie Wang, Chongruo Wu, Yong Jae Lee, and Panqu Wang. Hplflownet: Hierarchical permutohedral lattice flownet for scene flow estimation on large-scale point clouds. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3254-3263, 2019.
[15] Meng-Hao Guo, Jun-Xiong Cai, Zheng-Ning Liu, Tai-Jiang Mu, Ralph R Martin, and Shi-Min Hu. Pct: Point cloud transformer. Computational Visual Media, 7:187-199, 2021.
[16] Eddy Ilg, Nikolaus Mayer, Tonmoy Saikia, Margret Keuper, Alexey Dosovitskiy, and Thomas Brox. Flownet 2.0: Evolution of optical flow estimation with deep networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2462-2470, 2017.
[17] Yair Kittenplon, Yonina C Eldar, and Dan Raviv. Flowstep3d: Model unrolling for self-supervised scene flow estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4114-4123, 2021.
[18] Itai Lang, Dror Aiger, Forrester Cole, Shai Avidan, and Michael Rubinstein. Scoop: Self-supervised correspondence and optimization-based scene flow. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5281-5290, 2023.
[19] Bing Li, Cheng Zheng, Silvio Giancola, and Bernard Ghanem. Sctn: Sparse convolution-transformer network for scene flow estimation. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 1254-1262, 2022.