A hybrid quantum-classical algorithm for robust fitting
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 simulation 11 Source code: https://github.com/dadung/HQC-robust-fitting.
RAS ID
54187
Document Type
Conference Proceeding
Date of Publication
9-1-2022
Volume
2022-June
Funding Information
Australian Research Council
School
School of Science / Centre for Artificial Intelligence and Machine Learning (CAIML)
Grant Number
ARC Numbers : DP200101675, DP200103448
Copyright
free_to_read
Publisher
IEEE
Recommended Citation
Doan, A., Sasdelli, M., Suter, D., & Chin, T. (2022). A hybrid quantum-classical algorithm for robust fitting. DOI: https://doi.org/10.1109/CVPR52688.2022.00051
Comments
Doan, A. D., Sasdelli, M., Suter, D., & Chin, T. J. (2022). A hybrid quantum-classical algorithm for robust fitting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 417-427. https://doi.org/10.1109/CVPR52688.2022.00051
An open access version of this paper is provided by the Computer Vision Foundation.