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Continuous and Orientation-preserving Correspondences via Functional Maps

arXiv 1806.04455 ↗First posted 12 Jun 2018cs.GRcited by 77

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Abstract (arXiv)

We propose a method for efficiently computing orientation-preserving and approximately continuous correspondences between non-rigid shapes, using the functional maps framework. We first show how orientation preservation can be formulated directly in the functional (spectral) domain without using landmark or region correspondences and without relying on external symmetry information. This allows us to obtain functional maps that promote orientation preservation, even when using descriptors, that are invariant to orientation changes. We then show how higher quality, approximately continuous and bijective pointwise correspondences can be obtained from initial functional maps by introducing a novel refinement technique that aims to simultaneously improve the maps both in the spectral and spatial domains. This leads to a general pipeline for computing correspondences between shapes that results in high-quality maps, while admitting an efficient optimization scheme. We show through extensive evaluation that our approach improves upon state-of-the-art results on challenging isometric and non-isometric correspondence benchmarks according to both measures of continuity and coverage as well as producing semantically meaningful correspondences as measured by the distance to ground truth maps.

Key facts. Cited by 77 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Jun 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 9; 2022: 13; 2023: 24; 2024: 11; 2025: 10; 2026: 10.

77 papers.

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  1. 2021NeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Gocited by 81
  2. 2021Subdivision-Based Mesh Convolution Networkscited by 60
  3. 2023Unsupervised Learning of Robust Spectral Shape Matchingcited by 57
  4. 2022Unsupervised Deep Multi-Shape Matchingcited by 39
  5. 2022Smooth Non-Rigid Shape Matching via Effective Dirichlet Energy Optimizationcited by 37
  6. 2022Deep Orientation-Aware Functional Maps: Tackling Symmetry Issues in Shape Matchingcited by 36
  7. 2022Learning Multi-resolution Functional Maps with Spectral Attention for Robust Shape Matchingcited by 35
  8. 2023Zero-Shot 3D Shape Correspondencecited by 33
  9. 2023Spatially and Spectrally Consistent Deep Functional Mapscited by 30
  10. 2024Spectral Meets Spatial: Harmonising 3D Shape Matching and Interpolationcited by 26
  11. 2023Self-Supervised Learning for Multimodal Non-Rigid 3D Shape Matchingcited by 23
  12. 2023NICP: Neural ICP for 3D Human Registration at Scalecited by 21
38 more
  1. 2023Understanding and Improving Features Learned in Deep Functional Mapscited by 20
  2. 2022A Scalable Combinatorial Solver for Elastic Geometrically Consistent 3D Shape Matchingcited by 19
  3. 2023SE-ORNet: Self-Ensembling Orientation-aware Network for Unsupervised Point Cloud Shape Correspondencecited by 19
  4. 2022Elastic shape analysis of surfaces with second-order Sobolev metrics: a comprehensive numerical frameworkcited by 18
  5. 2023NCP: Neural Correspondence Prior for Effective Unsupervised Shape Matchingcited by 18
  6. 2023Hybrid Functional Maps for Crease-Aware Non-Isometric Shape Matchingcited by 18
  7. 2021Complex Functional Maps : a Conformal Link Between Tangent Bundlescited by 17
  8. 2024Partial-to-Partial Shape Matching with Geometric Consistencycited by 16
  9. 2024Memory-Scalable and Simplified Functional Map Learningcited by 14
  10. 2025Denoising Functional Maps: Diffusion Models for Shape Correspondencecited by 14
  11. 2025Hierarchical Neural Semantic Representation for 3D Semantic Correspondencecited by 14
  12. 2022Implicit field supervision for robust non-rigid shape matchingcited by 13
  13. 2022SRFeat: Learning Locally Accurate and Globally Consistent Non-Rigid Shape Correspondencecited by 13
  14. 2023Neural Semantic Surface Mapscited by 13
  15. 2023Geometrically Consistent Partial Shape Matchingcited by 13
  16. 2023Non-Rigid Shape Registration via Deep Functional Maps Priorcited by 12
  17. 2024Integrating Efficient Optimal Transport and Functional Maps For Unsupervised Shape Correspondence Learningcited by 12
  18. 2024SRIF: Semantic Shape Registration Empowered by Diffusion-based Image Morphing and Flow Estimationcited by 12
  19. 2024Wormhole Loss for Partial Shape Matchingcited by 12
  20. 2023GenCorres: Consistent Shape Matching via Coupled Implicit-Explicit Shape Generative Modelscited by 11
  21. 2023On Unsupervised Partial Shape Correspondencecited by 11
  22. 2024Finsler-Laplace-Beltrami Operators with Application to Shape Analysiscited by 10
  23. 2024DV-Matcher: Deformation-based Non-Rigid Point Cloud Matching Guided by Pre-trained Visual Featurescited by 10
  24. 2025DiffuMatch: Category-Agnostic Spectral Diffusion Priors for Robust Non-rigid Shape Matchingcited by 10
  25. 2023Neural Intrinsic Embedding for Non-rigid Point Cloud Matchingcited by 9
  26. 2022Non-Isometric Shape Matching via Functional Maps on Landmark-Adapted Basescited by 8
  27. 2022Reduced Representation of Deformation Fields for Effective Non-rigid Shape Matchingcited by 8
  28. 2022G-MSM: Unsupervised Multi-Shape Matching with Graph-based Affinity Priorscited by 8
  29. 2023Learning Harmonic Molecular Representations on Riemannian Manifoldcited by 8
  30. 2023Revisiting Map Relations for Unsupervised Non-Rigid Shape Matchingcited by 8
  31. 2021Deep Virtual Markers for Articulated 3D Shapescited by 7
  32. 2023DiscoMatch: Fast Discrete Optimisation for Geometrically Consistent 3D Shape Matchingcited by 7
  33. 2024Shape Non-rigid Kinematics (SNK): A Zero-Shot Method for Non-Rigid Shape Matching via Unsupervised Functional Map Regularized Reconstructioncited by 7
  34. 2024Synchronous Diffusion for Unsupervised Smooth Non-Rigid 3D Shape Matchingcited by 7
  35. 2023Generalizable Local Feature Pre-training for Deformable Shape Analysiscited by 6
  36. 2023Leveraging Intrinsic Properties for Non-Rigid Garment Alignmentcited by 6
  37. 2022Structured Prediction Problem Archivecited by 4
  38. 2023SIGMA: Scale-Invariant Global Sparse Shape Matchingcited by 4

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