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arXiv · 2110.02918

Boosting RANSAC via Dual Principal Component Pursuit

Yunchen Yang·Xinyue Zhang·Tianjiao Ding·Daniel P. Robinson·Rene Vidal·Manolis C. Tsakiris
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Abstract

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.

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Yunchen Yang, Xinyue Zhang, Tianjiao Ding, Daniel P. Robinson, Rene Vidal, Manolis C. Tsakiris. 2021-10-06. Boosting RANSAC via Dual Principal Component Pursuit. https://arxiv.org/abs/2110.02918

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