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Type: Conference paper
Title: A Hybrid Quantum-Classical Algorithm for Robust Fitting
Author: Doan, A.D.
Sasdelli, M.
Suter, D.
Chin, T.J.
Citation: Proceedings / CVPR, IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2022, vol.2022-June, pp.417-427
Publisher: IEEE
Publisher Place: Online
Issue Date: 2022
Series/Report no.: IEEE Conference on Computer Vision and Pattern Recognition
ISBN: 9781665469463
ISSN: 1063-6919
Conference Name: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (19 Jun 2022 - 24 Jun 2022 : New Orleans, Louisiana)
Statement of
Anh-Dzung Doan, Michele Sasdelli, David Suter, Tat-Jun Chin
Abstract: 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 that solves a sequence of integer programs and terminates with a global solution or an error bound. The combinatorial subproblems are amenable to a quantum annealer, which helps to tighten the bound efficiently. While our usage of quantum computing does not surmount the fundamental intractability of robust fitting, by providing error bounds our algorithm is a practical improvement over randomised heuristics. Moreover, our work represents a concrete application of quantum computing in computer vision. We present results obtained using an actual quantum computer (D-Wave Advantage) and via simulation1.
Keywords: Optimization methods
Rights: ©2022 IEEE
DOI: 10.1109/CVPR52688.2022.00051
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Appears in Collections:Computer Science publications

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