Geometric model fitting is a challenging but fundamental computer vision problem. Recently, quantum optimization has been shown to enhance robust fitting for the case of a single model, while leaving the question of multi-model fitting open. In response to this challenge, this paper shows that the latter case can significantly benefit from quantum hardware and proposes the first quantum approach to multi- model fitting (MMF). We formulate MMF as a problem that can be efficiently sampled by modern adiabatic quantum computers without the relaxation of the objective function. We also propose an iterative and decomposed version of our method, which supports real-world-sized problems. The experimental evaluation demonstrates promising results on a variety of datasets.


  • Paper, 4.6 MB

  • Poster, 1.8 MB

  • Source Code (github)



BibTeX, 1 KB

    author    = {Farina, Matteo and Magri, Luca and Menapace, Willi and Ricci, Elisa and Golyanik, Vladislav and Arrigoni, Federica}, 
    title     = {Quantum Multi-Model Fitting}, 
    booktitle = {Computer Vision and Pattern Recognition (CVPR)}, 
    year      = {2023} 


For questions, clarifications, please get in touch with:
Matteo Farina m.farina@unitn.it(regarding the source code)
Federica Ariggoni federica.arrigoni@polimi.it
Vladislav Golyanik golyanik@mpi-inf.mpg.de

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