mlsanityData to 7 Oct 2026

3D-CODED : 3D Correspondences by Deep Deformation

arXiv 1806.05228 ↗First posted 13 Jun 2018cs.CVcited by 160

Not on the map. Why?

Abstract (arXiv)

We present a new deep learning approach for matching deformable shapes by introducing it Shape Deformation Networks which jointly encode 3D shapes and correspondences. This is achieved by factoring the surface representation into (i) a template, that parameterizes the surface, and (ii) a learnt global feature vector that parameterizes the transformation of the template into the input surface. By predicting this feature for a new shape, we implicitly predict correspondences between this shape and the template. We show that these correspondences can be improved by an additional step which improves the shape feature by minimizing the Chamfer distance between the input and transformed template. We demonstrate that our simple approach improves on state-of-the-art results on the difficult FAUST-inter challenge, with an average correspondence error of 2.88cm. We show, on the TOSCA dataset, that our method is robust to many types of perturbations, and generalizes to non-human shapes. This robustness allows it to perform well on real unclean, meshes from the the SCAPE dataset.

Key facts. Cited by 160 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Jun 2018.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 41; 2022: 30; 2023: 36; 2024: 23; 2025: 18; 2026: 12.

160 papers.

Sort:

By area:

  1. 2021Neural Fields in Visual Computing and Beyondcited by 539
  2. 2022POCO: Point Convolution for Surface Reconstructioncited by 91
  3. 2021The Power of Points for Modeling Humans in Clothingcited by 90
  4. 2021NPMs: Neural Parametric Models for 3D Deformable Shapescited by 89
  5. 2021Lepard: Learning partial point cloud matching in rigid and deformable scenescited by 85
  6. 2021NeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Gocited by 81
  7. 2021SCALE: Modeling Clothed Humans with a Surface Codec of Articulated Local Elementscited by 72
  8. 2021Subdivision-Based Mesh Convolution Networkscited by 60
  9. 2022A Survey of Non-Rigid 3D Registrationcited by 59
  10. 2023Unsupervised Learning of Robust Spectral Shape Matchingcited by 57
  11. 2023Diffusion 3D Features (Diff3F): Decorating Untextured Shapes with Distilled Semantic Featurescited by 55
  12. 2021Shape registration in the time of transformerscited by 51
38 more
  1. 2021Locally Aware Piecewise Transformation Fields for 3D Human Mesh Registrationcited by 41
  2. 2021ARAPReg: An As-Rigid-As Possible Regularization Loss for Learning Deformable Shape Generatorscited by 40
  3. 2022Unsupervised Deep Multi-Shape Matchingcited by 39
  4. 2022Learned Vertex Descent: A New Direction for 3D Human Model Fittingcited by 37
  5. 2023Dynamic Point Fieldscited by 37
  6. 2021DeepMetaHandles: Learning Deformation Meta-Handles of 3D Meshes with Biharmonic Coordinatescited by 36
  7. 2022Deep Orientation-Aware Functional Maps: Tackling Symmetry Issues in Shape Matchingcited by 36
  8. 2022Learning Multi-resolution Functional Maps with Spectral Attention for Robust Shape Matchingcited by 35
  9. 2021Multiway Non-rigid Point Cloud Registration via Learned Functional Map Synchronizationcited by 30
  10. 2023Spatially and Spectrally Consistent Deep Functional Mapscited by 30
  11. 2022Skeleton-free Pose Transfer for Stylized 3D Characterscited by 29
  12. 2022Neural Jacobian Fields: Learning Intrinsic Mappings of Arbitrary Meshescited by 28
  13. 2022CATRE: Iterative Point Clouds Alignment for Category-level Object Pose Refinementcited by 28
  14. 2022SCOOP: Self-Supervised Correspondence and Optimization-Based Scene Flowcited by 28
  15. 2024Spectral Meets Spatial: Harmonising 3D Shape Matching and Interpolationcited by 26
  16. 2022Bending Graphs: Hierarchical Shape Matching using Gated Optimal Transportcited by 24
  17. 2022ACID: Action-Conditional Implicit Visual Dynamics for Deformable Object Manipulationcited by 24
  18. 2023Self-Supervised Learning for Multimodal Non-Rigid 3D Shape Matchingcited by 23
  19. 2024InterTrack: Tracking Human Object Interaction without Object Templatescited by 22
  20. 2021Learning 3D Dense Correspondence via Canonical Point Autoencodercited by 21
  21. 2023CloSET: Modeling Clothed Humans on Continuous Surface with Explicit Template Decompositioncited by 21
  22. 2023NICP: Neural ICP for 3D Human Registration at Scalecited by 21
  23. 2021Intrinsic-Extrinsic Preserved GANs for Unsupervised 3D Pose Transfercited by 20
  24. 2023Understanding and Improving Features Learned in Deep Functional Mapscited by 20
  25. 2021Augmenting Implicit Neural Shape Representations with Explicit Deformation Fieldscited by 19
  26. 2023SE-ORNet: Self-Ensembling Orientation-aware Network for Unsupervised Point Cloud Shape Correspondencecited by 19
  27. 2021VoteHMR: Occlusion-Aware Voting Network for Robust 3D Human Mesh Recovery from Partial Point Cloudscited by 18
  28. 2023MoSAR: Monocular Semi-Supervised Model for Avatar Reconstruction using Differentiable Shadingcited by 18
  29. 2021GLASS: Geometric Latent Augmentation for Shape Spacescited by 16
  30. 2021NeeDrop: Self-supervised Shape Representation from Sparse Point Clouds using Needle Droppingcited by 16
  31. 2021Field Convolutions for Surface CNNscited by 14
  32. 2023Deep Graph-based Spatial Consistency for Robust Non-rigid Point Cloud Registrationcited by 14
  33. 2023Generalizing Neural Human Fitting to Unseen Poses With Articulated SE(3) Equivariancecited by 14
  34. 2024Memory-Scalable and Simplified Functional Map Learningcited by 14
  35. 2025Denoising Functional Maps: Diffusion Models for Shape Correspondencecited by 14
  36. 2025Hierarchical Neural Semantic Representation for 3D Semantic Correspondencecited by 14
  37. 2021ResNet-LDDMM: Advancing the LDDMM Framework using Deep Residual Networkscited by 13
  38. 2022Implicit field supervision for robust non-rigid shape matchingcited by 13

50 most cited of 160 All 160 (JSON)