In this paper, we revisit the problem of local optimization in RANSAC. Once a so-far-the-best model has been found, we refine it via Dual Principal Component Pursuit (DPCP), a robust subspace learning method with strong theoretical support and efficient algorithms. The proposed DPCP-RANSAC has far fewer parameters than existing methods and is scalable. Experiments on estimating two-view homographies, fundamental and essential matrices, and three-view homographic tensors using large-scale datasets show that our approach consistently has higher accuracy than state-of-the-art alternatives.
翻译:在本文中,我们重新审视了RANSAC的本地优化问题。一旦找到一个最先进的模型,我们就通过双本组成部分追求(DPCP)来完善它,这是一种强有力的子空间学习方法,具有很强的理论支持和高效算法。拟议的DPCP-RANSAC的参数远远少于现有方法,而且可以伸缩。 使用大型数据集估算两眼同质、基本和基本矩阵以及三眼全息矩阵的实验显示,我们的方法始终比最新替代方法更准确。