Zorah Lähner
Zorah Lähner
Postdoctoral Researcher, University of Siegen
Verified email at uni-siegen.de - Homepage
Title
Cited by
Cited by
Year
Deepwrinkles: Accurate and realistic clothing modeling
Z Lahner, D Cremers, T Tung
Proceedings of the European Conference on Computer Vision (ECCV), 667-684, 2018
852018
Efficient deformable shape correspondence via kernel matching
M Vestner, Z Lähner, A Boyarski, O Litany, R Slossberg, T Remez, ...
2017 International Conference on 3D Vision (3DV), 517-526, 2017
472017
SHREC’16: Matching of deformable shapes with topological noise
Z Lähner, E Rodola, MM Bronstein, D Cremers, O Burghard, L Cosmo, ...
Proc. 3DOR 2 (10.2312), 2016
272016
Efficient globally optimal 2d-to-3d deformable shape matching
Z Lahner, E Rodola, FR Schmidt, MM Bronstein, D Cremers
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016
212016
Efficient deformable shape correspondence via kernel matching
Z Lähner, M Vestner, A Boyarski, O Litany, R Slossberg, T Remez, ...
arXiv preprint arXiv:1707.08991, 2017
15*2017
Efficient deformable shape correspondence via kernel matching
A Boyarski, A Bronstein, M Bronstein, D Cremers, R Kimmel, Z Lähner, ...
arXiv preprint arXiv 1707, 2017
12*2017
Divergence‐Free Shape Correspondence by Deformation
M Eisenberger, Z Lähner, D Cremers
Computer Graphics Forum 38 (5), 1-12, 2019
11*2019
Smooth shells: Multi-scale shape registration with functional maps
M Eisenberger, Z Lahner, D Cremers
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020
102020
Functional maps representation on product manifolds
E Rodolà, Z Lähner, AM Bronstein, MM Bronstein, J Solomon
Computer Graphics Forum 38 (1), 678-689, 2019
102019
Efficient deformable shape correspondence via kernel matching
Z Lähner, M Vestner, A Boyarski, O Litany, R Slossberg, T Remez, ...
arXiv preprint arXiv:1707.08991, 2017
92017
Shape correspondence with isometric and non-isometric deformations
RM Dyke, C Stride, YK Lai, PL Rosin, M Aubry, A Boyarski, AM Bronstein, ...
The Eurographics Association, 2019
8*2019
SHREC 16 Matching of Deformable Shapes with Topological Noise
Z Lahner, E Rodolà, M Bronstein, D Cremers, O Burghard, A Cosmo, ...
null, 2016
52016
Simulated annealing for 3d shape correspondence
B Holzschuh, Z Lähner, D Cremers
2020 International Conference on 3D Vision (3DV), 252-260, 2020
22020
Unsupervised dense shape correspondence using heat kernels
M Aygün, Z Lähner, D Cremers
2020 International Conference on 3D Vision (3DV), 573-582, 2020
22020
Training or Architecture? How to Incorporate Invariance in Neural Networks
KV Gandikota, J Geiping, Z Lähner, A Czapliński, M Moeller
arXiv preprint arXiv:2106.10044, 2021
2021
Training or Architecture? How to Incorporate Invariance in Neural Networks
K Vaishnavi Gandikota, J Geiping, Z Lähner, A Czapliński, M Moeller
arXiv e-prints, arXiv: 2106.10044, 2021
2021
Q-Match: Iterative Shape Matching via Quantum Annealing
MS Benkner, Z Lähner, V Golyanik, C Wunderlich, C Theobalt, M Moeller
arXiv preprint arXiv:2105.02878, 2021
2021
Q-Match: Iterative Shape Matching via Quantum Annealing
M Seelbach Benkner, Z Lähner, V Golyanik, C Wunderlich, C Theobalt, ...
arXiv e-prints, arXiv: 2105.02878, 2021
2021
Isometric Multi-Shape Matching
M Gao, Z Lahner, J Thunberg, D Cremers, F Bernard
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021
2021
Functional Maps on Product Manifolds.
E Rodolà, Z Lähner, AM Bronstein, MM Bronstein, J Solomon
SGP (Posters), 9-10, 2018
2018
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Articles 1–20