Boosting RANSAC via Dual Principal Component Pursuit
This work addresses robust model estimation in computer vision, offering a scalable solution with fewer parameters, though it appears incremental as it builds on existing RANSAC and DPCP methods.
The paper tackles the problem of local optimization in RANSAC by refining models using Dual Principal Component Pursuit, resulting in consistently higher accuracy than state-of-the-art methods in experiments on homographies and tensors.
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.