# Boosting RANSAC via Dual Principal Component Pursuit

arXiv: https://arxiv.org/abs/2110.02918 · HTML: https://mlsanity.com/paper/2110.02918/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 6 Oct 2021.

## Builds on (most cited first, 6 of 6)

- [Graph-Cut RANSAC](https://mlsanity.com/paper/1706.00984/) (2017)
- [An Overview of Robust Subspace Recovery](https://mlsanity.com/paper/1803.01013/) (2018)
- [Dual Principal Component Pursuit](https://mlsanity.com/paper/1510.04390/) (2015)
- [Finding a sparse vector in a subspace: Linear sparsity using alternating directions](https://mlsanity.com/paper/1412.4659/) (2014)
- [A Well-Tempered Landscape for Non-convex Robust Subspace Recovery](https://mlsanity.com/paper/1706.03896/) (2017)
- [Hyperplane Clustering Via Dual Principal Component Pursuit](https://mlsanity.com/paper/1706.01604/) (2017)

## Abstract (arXiv)

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.