mlsanityData to 14 Oct 2026

Complex Functional Maps : a Conformal Link Between Tangent Bundles

arXiv 2112.09546 ↗First posted 17 Dec 2021cs.CV math.DGcited by 17

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Abstract (arXiv)

In this paper, we introduce complex functional maps, which extend the functional map framework to conformal maps between tangent vector fields on surfaces. A key property of these maps is their orientation awareness. More specifically, we demonstrate that unlike regular functional maps that link functional spaces of two manifolds, our complex functional maps establish a link between oriented tangent bundles, thus permitting robust and efficient transfer of tangent vector fields. By first endowing and then exploiting the tangent bundle of each shape with a complex structure, the resulting operations become naturally orientationaware, thus favoring orientation and angle preserving correspondence across shapes, without relying on descriptors or extra regularization. Finally, and perhaps more importantly, we demonstrate how these objects enable several practical applications within the functional map framework. We show that functional maps and their complex counterparts can be estimated jointly to promote orientation preservation, regularizing pipelines that previously suffered from orientation-reversing symmetry errors.

Key facts. Cited by 17 papers in the map (data through 14 Oct 2026). Builds on 16 of its 64 references that are in the map. First posted on arXiv 17 Dec 2021.

Builds on

16 of 64 references.

References · 16

  1. 20163D Menagerie: Modeling the 3D shape and pose of animalscited by 312
  2. 2017Deep Functional Maps: Structured Prediction for Dense Shape Correspondencecited by 134
  3. 2020Deep Geometric Functional Maps: Robust Feature Learning for Shape Correspondencecited by 125
  4. 2018Continuous and Orientation-preserving Correspondences via Functional Mapscited by 77
  5. 2019Smooth Shells: Multi-Scale Shape Registration with Functional Mapscited by 72
  6. 2015Partial Functional Correspondencecited by 61
  7. 2018Multi-directional Geodesic Neural Networks via Equivariant Convolutioncited by 32
  8. 2018The Vector Heat Methodcited by 31
  9. 2019ZoomOut: Spectral Upsampling for Efficient Shape Correspondencecited by 30
  10. 2019Cyclic Functional Mapping: Self-supervised correspondence between non-isometric deformable shapescited by 30
  11. 2018FARM: Functional Automatic Registration Method for 3D Human Bodiescited by 26
  12. 2017Boundary First Flatteningcited by 17
4 more
  1. 2013Spectral Generalized Multi-Dimensional Scalingcited by 11
  2. 2020MapTree: Recovering Multiple Solutions in the Space of Mapscited by 11
  3. 2017Robust Structure-based Shape Correspondencecited by 7
  4. 2017Functional Characterization of Deformation Fieldscited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 5; 2024: 5; 2025: 1; 2026: 4.

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  1. 2023Unsupervised Learning of Robust Spectral Shape Matchingcited by 57
  2. 2022Deep Orientation-Aware Functional Maps: Tackling Symmetry Issues in Shape Matchingcited by 36
  3. 2024Spectral Meets Spatial: Harmonising 3D Shape Matching and Interpolationcited by 26
  4. 2023Hybrid Functional Maps for Crease-Aware Non-Isometric Shape Matchingcited by 18
  5. 2024DIMON: Learning Solution Operators of Partial Differential Equations on a Diffeomorphic Family of Domainscited by 16
  6. 2024Memory-Scalable and Simplified Functional Map Learningcited by 14
  7. 2023Single Mesh Diffusion Models with Field Latents for Texture Generationcited by 11
  8. 2025DiffuMatch: Category-Agnostic Spectral Diffusion Priors for Robust Non-rigid Shape Matchingcited by 10
  9. 2022G-MSM: Unsupervised Multi-Shape Matching with Graph-based Affinity Priorscited by 8
  10. 2023Revisiting Map Relations for Unsupervised Non-Rigid Shape Matchingcited by 8
  11. 2026Non-Rigid 3D Shape Correspondences: From Foundations to Open Challenges and Opportunitiescited by 4
  12. 2026RINO: Rotation-Invariant Non-Rigid Correspondencescited by 2
5 more
  1. 2024An Intrinsic Vector Heat Networkcited by 1
  2. 2026SGSoft: Learning Fused Semantic-Geometric Features for 3D Shape Correspondence via Template-Guided Soft Signalscited by 1
  3. 2023Extracting a functional representation from a dictionary for non-rigid shape matchingcited by 0
  4. 2024Deep Frequency-Aware Functional Maps for Robust Shape Matchingcited by 0
  5. 2026MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondencecited by 0