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. Source code: https://github.com/dadung/HQC-robust-fitting
翻译:将几何模型与外部污染数据相匹配是难以解决的。 许多计算机视觉系统依靠随机抽样超常方法来解决强健的安装问题,这不能提供最佳的保证和误差界限。 因此,关键是要制定新办法,缩小成本昂贵的精确解决方案与质量得不到质量保证的快速超常解决方案之间的差距。 在本文中,我们建议采用混合量子古典算法进行稳健的安装。 我们的核心贡献是一种新颖的稳健配方程式,它解决了整数程序序列,并且以全球解决方案或错误来结束。 复式子问题可以由量子脉冲器解决,有助于节制约束。 虽然我们量子计算方法的使用不能克服强健健的安装的基本不易性,但提供错误将我们的算法束缚在随机化的超自然理论上是一种实际的改进。 此外,我们的工作代表了计算机视觉中量子计算的具体应用。 我们展示了使用实际量子计算机(D-Wave Advantage)和模拟获得的结果。 源代码: https://github.com/dung/HC-robast-bastard-fastard-commating。