Functional Maps

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13 papers0 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Manifold Embedding of Deep Image Features for Image Matching via Neural Adjoint Maps
Nov 3, 2026Nafie El Amrani, Moritz Heep, Florian BernardImage Matching Functional Maps
University of Bonn Lamarr Institute
MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence
Jul 17, 2026Qinsong Li, Jing Meng, Haibo Wang +1Functional Maps Non-Rigid Shape Correspondence
Big Data Institute, Central South University, Changsha, Hunan 410083, China · School of Mathematics and Statistics, Central South University, Changsha, Hunan 410083, China
RealSkin: Spatio-Spectral Partial Neural Adjoint Maps for Image-to-3D Attribute Transfer
Jul 14, 2026Jing Li, Yawei Luo, Xiangze Meng +3Functional Maps Non-Rigid Shape Correspondence
Jilin University, China · Zhejiang University, China · North China University of Technology, China
Hyper-Network Neural Functional Maps for Unsupervised Robust 3D Shape Matching
Jun 29, 2026Dongliang Cao, Florian BernardFunctional Maps Non-Rigid Shape Correspondence
University of Bonn, Germany
Coarse-to-Fine: A Hybrid Self-Supervised Method for Non-rigid 3D Shape Matching
Jun 25, 2026Feifan Luo, Ting Li, Zhao Li +1Self-Supervised Learning Functional Maps
Zhejiang University, China · Zhejiang Lab, China · Wenzhou University of Technology, China
DeepShapeMatchingKit: Accelerated Functional Map Solver and Shape Matching Pipelines Revisited
Apr 11, 2026Yizheng Xie, Lennart Bastian, Congyue Deng +3Functional Maps Non-Rigid Shape Correspondence
Technical University of Munich · Simon Fraser University · Munich Center for Machine Learning +3
Non-Rigid 3D Shape Correspondences: From Foundations to Open Challenges and Opportunities
Apr 1, 2026Aleksei Zhuravlev, Lennart Bastian, Dongliang Cao +12Functional Maps Non-Rigid Shape Correspondence
MPI for Informatics · Imperial College London · Technical University of Munich +5
RINO: Rotation-Invariant Non-Rigid Correspondences
Mar 29, 2026Maolin Gao, Shao Jie Hu-Chen, Congyue Deng +3Functional Maps Non-Rigid Shape Correspondence
TUM · MCML · Stanford University +1
On the Spectral Geometry of Cross-Modal Representations: A Functional Map Diagnostic for Multimodal Alignment
Mar 28, 2026Krisanu SarkarFunctional Maps Cross-Modal Alignment
Indian Institute of Technology Bombay, Mumbai, India
From Feature Learning to Spectral Basis Learning: A Unifying and Flexible Framework for Efficient and Robust Shape Matching
Mar 24, 2026Feifan Luo, Hongyang ChenFunctional Maps Non-Rigid Shape Correspondence
College of Computer Science and Technology, Zhejiang University, China · Research Center for Computational Earth and Space Science, Zhejiang Lab, China
Unsupervised Contrastive Learning for Efficient and Robust Spectral Shape Matching
Mar 19, 2026Feifan Luo, Hongyang ChenFunctional Maps Non-Rigid Shape Correspondence
College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China · Research Center for Data Hub and Security, Zhejiang Lab, Hangzhou 311121, China
SGMatch: Semantic-Guided Non-Rigid Shape Matching with Flow Regularization
Mar 13, 2026Tianwei Ye, Xiaoguang Mei, Yifan Xia +3Functional Maps Geometric Representation Learning
Wuhan University, Wuhan 430072, China
Affordance Transfer Across Object Instances via Semantically Anchored Functional Map
Feb 16, 2026Xiaoxiang Dong, Weiming ZhiFunctional Maps 3D Shape Matching
Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA. · School of Computer Science, The University of Sydney, Australia. · Australian Centre for Robotics, The University of Sydney, Australia. +1

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Manifold Embedding of Deep Image Features for Image Matching via Neural Adjoint Maps

Nafie El Amrani, Moritz Heep, Florian Bernard

University of Bonn Lamarr Institute

Image Matching Functional Maps

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Estimating semantic correspondences between different object instances of similar categories in real-world images is a fundamental challenge in computer vision. While the recent progress in foundation models has significantly advanced solutions for such difficult image correspondence problems, respective methods are often insufficiently regularised. For example, common nearest-neighbour matching with foundation model features often results in noisy correspondences, which is particularly prominent when solving for dense correspondences. In this work, we tackle image matching via functional maps, which have been popularised for 3D shape matching due to their powerful spectral formalism utilising an efficient low-dimensional linear representation. However, functional maps require that domains (in our cases the rectangular image grids) are equipped with an informative and non-trivial geometry. To define a meaningful manifold structure for images, we use Galerkin's method from finite elements to derive a discrete Laplace-Beltrami operator (LBO) based on a manifold embedding of deep image features. Further, we leverage a neural adjoint map that allows to represent non-linear mappings. We demonstrate that our method sets the new state of the art among zero-shot image correspondence methods on multiple dense correspondence benchmarks of real-world images. Our code is publicly available: https://github.com/NafieAmrani/NAMI