  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. The source code is available at:
https://github.com/FarinaMatteo/qmmf.
