Boosting RANSAC via Dual Principal Component Pursuit

TL;DR

This paper introduces DPCP-RANSAC, a robust and scalable method that refines models in RANSAC using Dual Principal Component Pursuit, leading to higher accuracy in geometric estimation tasks.

Contribution

It presents a novel integration of DPCP into RANSAC for improved local optimization with fewer parameters and better scalability.

Findings

DPCP-RANSAC outperforms state-of-the-art methods in accuracy.

The method is scalable to large datasets.

It effectively refines models in geometric estimation tasks.

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.