% Key papers for Primitive-based shape abstraction (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{cvpr2017_shape_12633,
  title = {{Shape Completion Using 3D-Encoder-Predictor CNNs and Shape Synthesis}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
}

@inproceedings{cvpr2018_gvcnn_13141,
  title = {{GVCNN: Group-View Convolutional Neural Networks for 3D Shape Recognition}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Feng_GVCNN_Group-View_Convolutional_CVPR_2018_paper.html},
}

@inproceedings{cvpr2020_local_15845,
  title = {{Local Implicit Grid Representations for 3D Scenes}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{cvpr2017_syncspeccnn_12676,
  title = {{SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
}

@inproceedings{cvpr2020_local_15844,
  title = {{Local Deep Implicit Functions for 3D Shape}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{cvpr2017_3d_11996,
  title = {{3D Shape Segmentation With Projective Convolutional Networks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
}

@inproceedings{cvpr2017_learning_12415,
  title = {{Learning Shape Abstractions by Assembling Volumetric Primitives}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
}

@inproceedings{aaai2019_meshnet_103444,
  title = {{MeshNet: Mesh Neural Network for 3D Shape Representation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4840},
}

@inproceedings{cvpr2020_view_16451,
  title = {{View-GCN: View-Based Graph Convolutional Network for 3D Shape Analysis}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{cvpr2018_variational_13696,
  title = {{Variational Autoencoders for Deforming 3D Mesh Models}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Tan_Variational_Autoencoders_for_CVPR_2018_paper.html},
}

@inproceedings{cvpr2019_superquadrics_14885,
  title = {{Superquadrics Revisited: Learning 3D Shape Parsing Beyond Cuboids}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{cvpr2020_deep_15356,
  title = {{Deep Geometric Functional Maps: Robust Feature Learning for Shape Correspondence}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{cvpr2018_csgnet_12888,
  title = {{CSGNet: Neural Shape Parser for Constructive Solid Geometry}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Sharma_CSGNet_Neural_Shape_CVPR_2018_paper.html},
}

@inproceedings{cvpr2021_deformed_16845,
  title = {{Deformed Implicit Field: Modeling 3D Shapes With Learned Dense Correspondence}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{aaai2019_view_103456,
  title = {{View Inter-Prediction GAN: Unsupervised Representation Learning for 3D Shapes by Learning Global Shape Memories to Support Local View Predictions}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4852},
}

@inproceedings{cvpr2021_unsupervised_18080,
  title = {{Unsupervised 3D Shape Completion Through GAN Inversion}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{cvpr2018_factoring_13097,
  title = {{Factoring Shape, Pose, and Layout From the 2D Image of a 3D Scene}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Tulsiani_Factoring_Shape_Pose_CVPR_2018_paper.html},
}

@inproceedings{cvpr2018_learning_13276,
  title = {{Learning Descriptor Networks for 3D Shape Synthesis and Analysis}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Xie_Learning_Descriptor_Networks_CVPR_2018_paper.html},
}

@inproceedings{aaai2019_mlvcnn_103472,
  title = {{MLVCNN: Multi-Loop-View Convolutional Neural Network for 3D Shape Retrieval}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4869},
}

@inproceedings{cvpr2021_deep_16822,
  title = {{Deep Implicit Templates for 3D Shape Representation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{cvpr2024_diffusion_23200,
  title = {{Diffusion 3D Features (Diff3F): Decorating Untextured Shapes with Distilled Semantic Features}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/29710},
}

@inproceedings{cvpr2024_partdistill_24440,
  title = {{PartDistill: 3D Shape Part Segmentation by Vision-Language Model Distillation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30350},
}

@inproceedings{cvpr2025_common3d_25743,
  title = {{Common3D: Self-Supervised Learning of 3D Morphable Models for Common Objects in Neural Feature Space}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/32989},
}

@inproceedings{icml2025_geometry_48538,
  title = {{Geometry-Informed Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44083},
}

@inproceedings{aaai2026_learning_100373,
  title = {{Learning Compact Latent Space for Representing Neural Signed Distance Functions with High-fidelity Geometry Details}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/37220},
}

@inproceedings{aaai2026_mdnd_102617,
  title = {{MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/37562},
}

@inproceedings{iclr2024_an_33644,
  title = {{An Intuitive Multi-Frequency Feature Representation for SO(3)-Equivariant Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19431},
}

@inproceedings{iclr2024_texttt_35590,
  title = {{\$textttNAISR\$: A 3D Neural Additive Model for Interpretable Shape Representation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17483},
}

@inproceedings{icml2026_from_55520,
  title = {{From Extrinsic to Intrinsic: Geodesic-Guided Representation Learning for 3D Geometric Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=zupxQlD4cw},
}

@inproceedings{neurips2024_neural_73570,
  title = {{Neural Isometries: Taming Transformations for Equivariant ML}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93912},
}

@inproceedings{neurips2025_learning_78585,
  title = {{Learning Generalizable Shape Completion with SIM(3) Equivariance}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=gEpXPbX8VV},
}

@inproceedings{neurips2025_shapeembed_78799,
  title = {{ShapeEmbed: a self-supervised learning framework for 2D contour quantification}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=iT2ZisemFs},
}

@inproceedings{iclr2026_interpretable_86920,
  title = {{Interpretable 3D Neural Object Volumes for Robust Conceptual Reasoning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=VSPLa2Sito},
}

@inproceedings{cvpr2026_sgsoft_97571,
  title = {{SGSoft: Learning Fused Semantic-Geometric Features for 3D Shape Correspondence via Template-Guided Soft Signals}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2026},
  url = {https://openaccess.thecvf.com/content/CVPR2026/html/Yoon_SGSoft_Learning_Fused_Semantic-Geometric_Features_for_3D_Shape_Correspondence_via_CVPR_2026_paper.html},
}

@inproceedings{aaai2026_dilo_104617,
  title = {{DiLO: Disentangled Latent Optimization for Learning Shape and Deformation in Grouped Deforming 3D Objects}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/37921},
}

@inproceedings{icml2026_equicad_50747,
  title = {{EquiCAD: A Geometric Equivariant Neural Network for 3D Shape Classification}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=FK2SZbz1YE},
}

@inproceedings{neurips2026_training_89992,
  title = {{Training-free Spatially Grounded Geometric Shape Encoding}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/150307},
}