Boosting RANSAC via Dual Principal Component Pursuit

• URL: http://arxiv.org/abs/2110.02918v1
• Date: Wed, 6 Oct 2021 17:04:45 GMT
• Title: Boosting RANSAC via Dual Principal Component Pursuit
• Authors: Yunchen Yang, Xinyue Zhang, Tianjiao Ding, Daniel P. Robinson, Rene Vidal, Manolis C. Tsakiris
• Abstract summary: We introduce Dual Principal Component Pursuit (DPCP) as a robust subspace learning method with strong theoretical support and efficient algorithms. Experiments on estimating two-view homographies, fundamental and essential matrices, and three-view homographic tensors show that our approach consistently has higher accuracy than state-of-the-art alternatives.
• Score: 24.942079487458624
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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