[37] Yuki Kawana, Yusuke Mukuta, and Tatsuya Harada. Unsupervised pose-aware part decomposition for 3d articulated objects. arXiv preprint arXiv:2110.04411, 2021.
[38] Durk P Kingma and Prafulla Dhariwal. Glow: Generative flow with invertible 1x1 convolutions. Advances in neural information processing systems, 31, 2018.
[39] Risi Kondor and Shubhendu Trivedi. On the generalization of equivariance and convolution in neural networks to the action of compact groups. In International Conference on Machine Learning, pages 2747-2755. PMLR, 2018.
[40] Jiahui Lei and Kostas Daniilidis. Cadex: Learning canonical deformation coordinate space for dynamic surface representation via neural homeomorphism. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022.
[41] Jiahui Lei, Congyue Deng, Karl Schmeckpeper, Leonidas Guibas, and Kostas Daniilidis. Efem: Equivariant neural field expectation maximization for 3d object segmentation without scene supervision. arXiv preprint arXiv:2303.15440, 2023.
[42] Jiahan Li, Shitong Luo, Congyue Deng, Chaoran Cheng, Jiaqi Guan, Leonidas Guibas, Jian Peng, and Jianzhu Ma. Directed weight neural networks for protein structure representation learning. arXiv preprint arXiv:2201.13299, 2022.
[43] Ruibo Li, Guosheng Lin, and Lihua Xie. Self-point-flow: Self-supervised scene flow estimation from point clouds with optimal transport and random walk. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 15577-15586, 2021.
[44] Xiaolong Li, Yijia Weng, Li Yi, Leonidas J Guibas, A Abbott, Shuran Song, and He Wang. Leveraging se (3) equivariance for self-supervised category-level object pose estimation from point clouds. Advances in Neural Information Processing Systems, 34:15370-15381, 2021.
[45] Qianli Liao and Tomaso Poggio. Bridging the gaps between residual learning, recurrent neural networks and visual cortex. arXiv preprint arXiv:1604.03640, 2016.
[46] Cheng-Wei Lin, Tung-I Chen, Hsin-Ying Lee, Wen-Chin Chen, and Winston H Hsu. Coarse-to-fine point cloud registration with se (3)-equivariant representations. arXiv preprint arXiv:2210.02045, 2022.
[47] Fangyu Liu, Shuaipeng Li, Liqiang Zhang, Chenghu Zhou, Rongtian Ye, Yuebin Wang, and Jiwen Lu. 3dcnn-dqn-rnn: A deep reinforcement learning framework for semantic parsing of large-scale 3d point clouds. In Proceedings of the IEEE international conference on computer vision, pages 5678-5687, 2017.
[48] Xingyu Liu, Charles R Qi, and Leonidas J Guibas. Flownet3d: Learning scene flow in 3d point clouds. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 529-537, 2019.
[49] Xingyu Liu, Mengyuan Yan, and Jeannette Bohg. Meteornet: Deep learning on dynamic 3d point cloud sequences. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9246-9255, 2019.
[50] Xueyi Liu, Ji Zhang, Ruizhen Hu, Haibin Huang, He Wang, and Li Yi. Self-supervised category-level articulated object pose estimation with part-level se (3) equivariance. arXiv preprint arXiv:2302.14268, 2023.
[51] Shitong Luo and Wei Hu. Diffusion probabilistic models for 3d point cloud generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2837-2845, 2021.
[52] Kaichun Mo, Shilin Zhu, Angel X Chang, Li Yi, Subarna Tripathi, Leonidas J Guibas, and Hao Su. Partnet: A large-scale benchmark for fine-grained and hierarchical part-level 3d object understanding. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 909–918, 2019.
[53] Jiteng Mu, Weichao Qiu, Adam Kortylewski, Alan Yuille, Nuno Vasconcelos, and Xiaolong Wang. A-sdf: Learning disentangled signed distance functions for articulated shape representation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 13001-13011, 2021.
[54] Haoran Pan, Jun Zhou, Yuanpeng Liu, Xuequan Lu, Weiming Wang, Xuefeng Yan, and Mingqiang Wei. So (3)-pose: So (3)-equivariance learning for 6d object pose estimation. arXiv preprint arXiv:2208.08338, 2022.