[74] Xiaogang Wang, Bin Zhou, Yahao Shi, Xiaowu Chen, Qinping Zhao, and Kai Xu. Shape2motion: Joint analysis of motion parts and attributes from 3d shapes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8876-8884, 2019.
[75] Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, and Justin M Solomon. Dynamic graph cnn for learning on point clouds. Acm Transactions On Graphics (tog), 38(5):1-12, 2019.
[76] Yuqi Wang, Yuntao Chen, and ZHAO-XIANG ZHANG. 4d unsupervised object discovery. Advances in Neural Information Processing Systems, 35:35563-35575, 2022.
[77] Joe H Ward Jr. Hierarchical grouping to optimize an objective function. Journal of the American statistical association, 58(301):236-244, 1963.
[78] Qiuhong Anna Wei, Sijie Ding, Jeong Joon Park, Rahul Sajnani, Adrien Poulenard, Srinath Sridhar, and Leonidas Guibas. Lego-net: Learning regular rearrangements of objects in rooms. arXiv e-prints, pages arXiv-2301, 2023.
[79] Maurice Weiler, Patrick Forre, Erik Verlinde, and Max Welling. Coordinate independent convolutional networks-isometry and gauge equivariant convolutions on riemannian manifolds. arXiv preprint arXiv:2106.06020, 2021.
[80] Thomas Weng, David Held, Franziska Meier, and Mustafa Mukadam. Neural grasp distance fields for robot manipulation. arXiv preprint arXiv:2211.02647, 2022.
[81] Yinshuang Xu, Jiahui Lei, Edgar Dobriban, and Kostas Daniilidis. Unified fourier-based kernel and nonlinearity design for equivariant networks on homogeneous spaces. In International Conference on Machine Learning, pages 24596-24614. PMLR, 2022.
[82] Zhengrong Xue, Zhecheng Yuan, Jiashun Wang, Xueqian Wang, Yang Gao, and Huazhe Xu. Usek: Unsupervised se (3)-equivariant 3d keypoints for generalizable manipulation. arXiv preprint arXiv:2209.13864, 2022.
[83] Guandao Yang, Xun Huang, Zekun Hao, Ming-Yu Liu, Serge Belongie, and Bharath Hariharan. Pointflow: 3d point cloud generation with continuous normalizing flows. In Proceedings of the IEEE/CVF international conference on computer vision, pages 4541-4550, 2019.
[84] Xiaoqing Ye, Jiamao Li, Hexiao Huang, Liang Du, and Xiaolin Zhang. 3d recurrent neural networks with context fusion for point cloud semantic segmentation. In Proceedings of the European conference on computer vision (ECCV), pages 403-417, 2018.
[85] Li Yi, Haibin Huang, Difan Liu, Evangelos Kalogerakis, Hao Su, and Leonidas Guibas. Deep part induction from articulated object pairs. arXiv preprint arXiv:1809.07417, 2018.
[86] Hong-Xing Yu, Jiajun Wu, and Li Yi. Rotationally equivariant 3d object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1456-1464, 2022.
[87] Bohang Zhang, Du Jiang, Di He, and Liwei Wang. Rethinking lipschitz neural networks and certified robustness: A boolean function perspective. In Advances in Neural Information Processing Systems, 2022.
[88] Linqi Zhou, Yilun Du, and Jiajun Wu. 3d shape generation and completion through point-voxel diffusion. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5826-5835, 2021.
[89] Minghan Zhu, Maani Ghaffari, and Huei Peng. Correspondence-free point cloud registration with so (3)-equivariant implicit shape representations. In Conference on Robot Learning, pages 1412-1422. PMLR, 2022.