% Key papers for Equivariant point cloud networks (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{cvpr2019_on_14604,
  title = {{On the Continuity of Rotation Representations in Neural Networks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{neurips2017_deep_56246,
  title = {{Deep Sets}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9123},
}

@inproceedings{cvpr2018_deep_12977,
  title = {{Deep Parametric Continuous Convolutional Neural Networks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Wang_Deep_Parametric_Continuous_CVPR_2018_paper.html},
}

@inproceedings{neurips2020_se_60760,
  title = {{SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17068},
}

@inproceedings{cvpr2020_acne_15094,
  title = {{ACNe: Attentive Context Normalization for Robust Permutation-Equivariant Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{cvpr2021_uv_18061,
  title = {{UV-Net: Learning From Boundary Representations}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{cvpr2021_brepnet_16628,
  title = {{BRepNet: A Topological Message Passing System for Solid Models}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{cvpr2021_equivariant_16958,
  title = {{Equivariant Point Network for 3D Point Cloud Analysis}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{neurips2019_symmetry_59050,
  title = {{Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13839},
}

@inproceedings{icml2020_lorentz_38430,
  title = {{Lorentz Group Equivariant Neural Network for Particle Physics}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/5843},
}

@inproceedings{cvpr2021_setvae_17885,
  title = {{SetVAE: Learning Hierarchical Composition for Generative Modeling of Set-Structured Data}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{icml2020_on_38536,
  title = {{On Learning Sets of Symmetric Elements}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6022},
}

@inproceedings{cvpr2018_ldmnet_13250,
  title = {{LDMNet: Low Dimensional Manifold Regularized Neural Networks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Zhu_LDMNet_Low_Dimensional_CVPR_2018_paper.html},
}

@inproceedings{cvpr2021_a_16535,
  title = {{A Functional Approach to Rotation Equivariant Non-Linearities for Tensor Field Networks.}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{cvpr2024_himap_23659,
  title = {{HIMap: HybrId Representation Learning for End-to-end Vectorized HD Map Construction}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30996},
}

@inproceedings{neurips2021_se_62644,
  title = {{SE(3)-equivariant prediction of molecular wavefunctions and electronic densities}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27761},
}

@inproceedings{iclr2021_on_30014,
  title = {{On the Universality of Rotation Equivariant Point Cloud Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/3011},
}

@inproceedings{cvpr2023_recognizing_22006,
  title = {{Recognizing Rigid Patterns of Unlabeled Point Clouds by Complete and Continuous Isometry Invariants With No False Negatives and No False Positives}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/21036},
}

@inproceedings{neurips2020_exchangeable_59815,
  title = {{Exchangeable Neural ODE for Set Modeling}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17749},
}

@inproceedings{neurips2022_on_64326,
  title = {{On the Effectiveness of Persistent Homology}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/53158},
}

@inproceedings{cvpr2024_local_24020,
  title = {{Local-consistent Transformation Learning for Rotation-invariant Point Cloud Analysis}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/29954},
}

@inproceedings{cvpr2024_generative_23608,
  title = {{Generative 3D Part Assembly via Part-Whole-Hierarchy Message Passing}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/29715},
}

@inproceedings{iclr2024_fast_34815,
  title = {{Fast, Expressive \$mathrmSE(n)\$ Equivariant Networks through Weight-Sharing in Position-Orientation Space}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18259},
}

@inproceedings{aaai2025_generalized_9320,
  title = {{Generalized Dimension Reduction Using Semi-Relaxed Gromov-Wasserstein Distance}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33766},
}

@inproceedings{cvpr2024_pose_24496,
  title = {{Pose-Transformed Equivariant Network for 3D Point Trajectory Prediction}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/31664},
}

@inproceedings{neurips2024_aligning_70431,
  title = {{Aligning Embeddings and Geometric Random Graphs: Informational Results and Computational Approaches for the Procrustes-Wasserstein Problem}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96674},
}

@inproceedings{neurips2024_conjugate_72559,
  title = {{Conjugate Bayesian Two-step Change Point Detection for Hawkes Process}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94813},
}

@inproceedings{iclr2024_differentiable_34251,
  title = {{Differentiable Euler Characteristic Transforms for Shape Classification}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18824},
}

@inproceedings{icml2024_mean_45246,
  title = {{Mean-field Chaos Diffusion Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33206},
}

@inproceedings{icml2025_an_47886,
  title = {{An in depth look at the Procrustes-Wasserstein distance: properties and barycenters}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44717},
}

@inproceedings{neurips2024_se_71215,
  title = {{SE(3)-bi-equivariant Transformers for Point Cloud Assembly}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96020},
}

@inproceedings{neurips2025_copresheaf_75004,
  title = {{Copresheaf Topological Neural Networks: A Generalized Deep Learning Framework}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=3G56xClPYg},
}

@inproceedings{neurips2025_topopoint_75789,
  title = {{TopoPoint: Enhance Topology Reasoning via Endpoint Detection in Autonomous Driving}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=C2fJE8t0lH},
}

@inproceedings{neurips2025_rigno_78060,
  title = {{RIGNO: A Graph-based Framework For Robust And Accurate Operator Learning For PDEs On Arbitrary Domains}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=ahJfROJOYt},
}

@inproceedings{iclr2025_on_80795,
  title = {{On the Completeness of Invariant Geometric Deep Learning Models}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/30973},
}

@inproceedings{iclr2025_e_83355,
  title = {{E(3)-equivariant models cannot learn chirality: Field-based molecular generation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/28460},
}

@inproceedings{aaai2025_exploring_9082,
  title = {{Exploring Topological Properties in Artificial Neural Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/35338},
}

@inproceedings{iclr2024_decodable_34396,
  title = {{Decodable and Sample Invariant Continuous Object Encoder}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18679},
}

@inproceedings{iclr2024_quasi_34605,
  title = {{Quasi-Monte Carlo for 3D Sliced Wasserstein}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18469},
}