Quantum Multi-Model Fitting
This work addresses a fundamental challenge in computer vision for researchers and practitioners, but it appears incremental as it extends quantum optimization from single-model to multi-model fitting.
The paper tackles the problem of multi-model fitting in computer vision by proposing the first quantum approach, which can be efficiently sampled by adiabatic quantum computers without relaxing the objective function, and demonstrates promising results on various datasets.
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.