Zorah Lähner

According to our database, Zorah Lähner authored at least 30 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Implicit-ARAP: Efficient Handle-Guided Deformation of High-Resolution Meshes and Neural Fields via Local Patch Meshing.

Daniele Baieri, Filippo Maggioli, Zorah Lähner, Simone Melzi, Emanuele Rodolà

CoRR, 2024

Synchronous Diffusion for Unsupervised Smooth Non-rigid 3D Shape Matching.

Dongliang Cao, Zorah Lähner, Florian Bernard

Proceedings of the Computer Vision - ECCV 2024, 2024

Hybrid Functional Maps for Crease-Aware Non-Isometric Shape Matching.

Lennart Bastian, Yizheng Xie, Nassir Navab, Zorah Lähner

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023

Kissing to Find a Match: Efficient Low-Rank Permutation Representation.

Hannah Dröge, Zorah Lähner, Yuval Bahat, Onofre Martorell, Felix Heide, Michael Möller

CoRR, 2023

SIGMA: Scale-Invariant Global Sparse Shape Matching.

Maolin Gao, Paul Roetzer, Marvin Eisenberger, Zorah Lähner, Michael Möller, Daniel Cremers, Florian Bernard

CoRR, 2023

On the Direct Alignment of Latent Spaces.

Zorah Lähner, Michael Moeller

Proceedings of UniReps: the First Workshop on Unifying Representations in Neural Models, 2023

Kissing to Find a Match: Efficient Low-Rank Permutation Representation.

Hannah Dröge, Zorah Lähner, Yuval Bahat, Onofre Martorell Nadal, Felix Heide, Michael Moeller

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

QuAnt: Quantum Annealing with Learnt Couplings.

Marcel Seelbach Benkner, Maximilian Krahn, Edith Tretschk, Zorah Lähner, Michael Moeller, Vladislav Golyanik

Proceedings of the Eleventh International Conference on Learning Representations, 2023

ΣIGMA: Scale-Invariant Global Sparse Shape Matching.

Maolin Gao, Paul Roetzer, Marvin Eisenberger, Zorah Lähner, Michael Möller, Daniel Cremers, Florian Bernard

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

A Network Analysis for Correspondence Learning via Linearly-Embedded Functions.

Sharik Siddiqi, Zorah Lähner

Proceedings of the Pattern Recognition - 45th DAGM German Conference, 2023

Conjugate Product Graphs for Globally Optimal 2D-3D Shape Matching.

Paul Roetzer, Zorah Lähner, Florian Bernard

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

CCuantuMM: Cycle-Consistent Quantum-Hybrid Matching of Multiple Shapes.

Harshil Bhatia, Edith Tretschk, Zorah Lähner, Marcel Seelbach Benkner, Michael Moeller, Christian Theobalt, Vladislav Golyanik

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

Intrinsic Neural Fields: Learning Functions on Manifolds.

Lukas Koestler, Daniel Grittner, Michael Möller, Daniel Cremers, Zorah Lähner

Proceedings of the Computer Vision - ECCV 2022, 2022

A Simple Strategy to Provable Invariance via Orbit Mapping.

Kanchana Vaishnavi Gandikota, Jonas Geiping, Zorah Lähner, Adam Czaplinski, Michael Möller

Proceedings of the Computer Vision - ACCV 2022, 2022

2021

Continuous Correspondence of Non-Rigid 3D Shapes.

Zorah Lähner

PhD thesis, 2021

Training or Architecture? How to Incorporate Invariance in Neural Networks.

Kanchana Vaishnavi Gandikota, Jonas Geiping, Zorah Lähner, Adam Czaplinski, Michael Möller

CoRR, 2021

Q-Match: Iterative Shape Matching via Quantum Annealing.

Marcel Seelbach Benkner, Zorah Lähner, Vladislav Golyanik, Christof Wunderlich, Christian Theobalt, Michael Moeller

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

Isometric Multi-Shape Matching.

Maolin Gao, Zorah Lähner, Johan Thunberg, Daniel Cremers, Florian Bernard

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020

Smooth Shells: Multi-Scale Shape Registration With Functional Maps.

Marvin Eisenberger, Zorah Lähner, Daniel Cremers

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

Simulated Annealing for 3D Shape Correspondence.

Benjamin Holzschuh, Zorah Lähner, Daniel Cremers

Proceedings of the 8th International Conference on 3D Vision, 2020

Unsupervised Dense Shape Correspondence using Heat Kernels.

Mehmet Aygün, Zorah Lähner, Daniel Cremers

Proceedings of the 8th International Conference on 3D Vision, 2020

2019

Functional Maps Representation On Product Manifolds.

Emanuele Rodolà, Zorah Lähner, Alexander M. Bronstein, Michael M. Bronstein, Justin Solomon

Comput. Graph. Forum, 2019

Divergence-Free Shape Correspondence by Deformation.

Marvin Eisenberger, Zorah Lähner, Daniel Cremers

Comput. Graph. Forum, 2019

Shape Correspondence with Isometric and Non-Isometric Deformations.

Roberto M. Dyke, C. Stride, Yu-Kun Lai, Paul L. Rosin, Mathieu Aubry, Amit Boyarski, Alexander M. Bronstein, Michael M. Bronstein, Daniel Cremers, Matthew Fisher, Thibault Groueix, Daoliang Guo, Vladimir G. Kim, Ron Kimmel, Zorah Lähner, Kun Li, Or Litany, Tal Remez, Emanuele Rodolà, Bryan C. Russell, Yusuf Sahillioglu, Ron Slossberg, Gary K. L. Tam, Matthias Vestner, Z. Wu, Jingyu Yang

Proceedings of the 12th Eurographics Workshop on 3D Object Retrieval, 2019

2018

Divergence-Free Shape Interpolation and Correspondence.

Marvin Eisenberger, Zorah Lähner, Daniel Cremers

CoRR, 2018

Functional Maps on Product Manifolds.

Emanuele Rodolà, Zorah Lähner, Alex M. Bronstein, Michael M. Bronstein, Justin Solomon

Proceedings of the 16th Eurographics Symposium on Geometry Processing, 2018

DeepWrinkles: Accurate and Realistic Clothing Modeling.

Zorah Lähner, Daniel Cremers, Tony Tung

Proceedings of the Computer Vision - ECCV 2018, 2018

2017

Efficient Deformable Shape Correspondence via Kernel Matching.

Matthias Vestner, Zorah Lähner, Amit Boyarski, Or Litany, Ron Slossberg, Tal Remez, Emanuele Rodolà, Alexander M. Bronstein, Michael M. Bronstein, Ron Kimmel, Daniel Cremers

Proceedings of the 2017 International Conference on 3D Vision, 2017

2016

Efficient Globally Optimal 2D-to-3D Deformable Shape Matching.

Zorah Lähner, Emanuele Rodolà, Frank R. Schmidt, Michael M. Bronstein, Daniel Cremers

Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016

Matching of Deformable Shapes with Topological Noise.

Zorah Lähner, Emanuele Rodolà, Michael M. Bronstein, Daniel Cremers, Oliver Burghard, Luca Cosmo, Alexander Dieckmann, Reinhard Klein, Yusuf Sahillioglu

Proceedings of the 9th Eurographics Workshop on 3D Object Retrieval, 2016