A Hybrid Quantum-Classical Algorithm for Robust Fitting. (arXiv:2201.10110v1 [cs.CV])

Web: http://arxiv.org/abs/2201.10110

Fitting geometric models onto outlier contaminated data is provably
intractable. Many computer vision systems rely on random sampling heuristics to
solve robust fitting, which do not provide optimality guarantees and error
bounds. It is therefore critical to develop novel approaches that can bridge
the gap between exact solutions that are costly, and fast heuristics that offer
no quality assurances. In this paper, we propose a hybrid quantum-classical
algorithm for robust fitting. Our core contribution is a novel robust fitting
formulation …

algorithm arxiv cv hybrid