Maximum Consensus Parameter Estimation by Reweighted $\ell_1$ Methods

TL;DR

This paper introduces a simple, efficient reweighted algorithm for maximum consensus parameter estimation in computer vision, capable of often finding the global solution faster than existing methods.

Contribution

It proposes a novel smooth surrogate function and an iterative reweighted algorithm that improves efficiency and solution quality for MaxCon problems.

Findings

Algorithm is highly efficient and often finds the global solution.

Outperforms randomized methods in speed and accuracy.

Convergence analysis highlights fundamental differences from existing methods.

Abstract

Robust parameter estimation in computer vision is frequently accomplished by solving the maximum consensus (MaxCon) problem. Widely used randomized methods for MaxCon, however, can only produce {random} approximate solutions, while global methods are too slow to exercise on realistic problem sizes. Here we analyse MaxCon as iterative reweighted algorithms on the data residuals. We propose a smooth surrogate function, the minimization of which leads to an extremely simple iteratively reweighted algorithm for MaxCon. We show that our algorithm is very efficient and in many cases, yields the global solution. This makes it an attractive alternative for randomized methods and global optimizers. The convergence analysis of our method and its fundamental differences from the other iteratively reweighted methods are also presented.