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

no code implementations • CVPR 2023 • Harshil Bhatia, Edith Tretschk, Zorah Lähner, Marcel Seelbach Benkner, Michael Moeller, Christian Theobalt, Vladislav Golyanik

Jointly matching multiple, non-rigidly deformed 3D shapes is a challenging, $\mathcal{NP}$-hard problem.

QuAnt: Quantum Annealing with Learnt Couplings

no code implementations • 13 Oct 2022 • Marcel Seelbach Benkner, Maximilian Krahn, Edith Tretschk, Zorah Lähner, Michael Moeller, Vladislav Golyanik

As a result, the solution encodings can be chosen flexibly and compactly.

3D Rotation Estimation Graph Matching

Quantum Motion Segmentation

no code implementations • 24 Mar 2022 • Federica Arrigoni, Willi Menapace, Marcel Seelbach Benkner, Elisa Ricci, Vladislav Golyanik

Motion segmentation is a challenging problem that seeks to identify independent motions in two or several input images.

Motion Segmentation Segmentation

Adiabatic Quantum Graph Matching with Permutation Matrix Constraints

no code implementations • 8 Jul 2021 • Marcel Seelbach Benkner, Vladislav Golyanik, Christian Theobalt, Michael Moeller

In this work, we address such problems with emerging quantum computing technology and propose several reformulations of QAPs as unconstrained problems suitable for efficient execution on quantum hardware.

Graph Matching valid

Q-Match: Iterative Shape Matching via Quantum Annealing

no code implementations • ICCV 2021 • Marcel Seelbach Benkner, Zorah Lähner, Vladislav Golyanik, Christof Wunderlich, Christian Theobalt, Michael Moeller

Finding shape correspondences can be formulated as an NP-hard quadratic assignment problem (QAP) that becomes infeasible for shapes with high sampling density.