Sparse resultant based minimal solvers in computer vision and their connection with the action matrix

TL;DR

This paper introduces an iterative scheme using sparse resultants and an extra polynomial to develop more efficient and stable minimal solvers for camera geometry problems in computer vision, offering a competitive alternative to existing methods.

Contribution

It proposes a novel iterative approach with an extra polynomial to improve solver efficiency and stability, connecting sparse resultants with action matrix methods in minimal problems.

Findings

The method produces smaller, more stable solvers than Grobner basis-based approaches.

It can be fully automated and integrated into existing solver generation tools.

Conditions for equivalence between action matrix and resultant-based solvers are analyzed.

Abstract

Many computer vision applications require robust and efficient estimation of camera geometry from a minimal number of input data measurements, i.e., solving minimal problems in a RANSAC framework. Minimal problems are usually formulated as complex systems of sparse polynomials. The systems usually are overdetermined and consist of polynomials with algebraically constrained coefficients. Most state-of-the-art efficient polynomial solvers are based on the action matrix method that has been automated and highly optimized in recent years. On the other hand, the alternative theory of sparse resultants and Newton polytopes has been less successful for generating efficient solvers, primarily because the polytopes do not respect the constraints on the coefficients. Therefore, in this paper, we propose a simple iterative scheme to test various subsets of the Newton polytopes and search for the most efficient solver. Moreover, we propose to use an extra polynomial with a special form to further improve the solver efficiency via a Schur complement computation. We show that for some camera geometry problems our extra polynomial-based method leads to smaller and more stable solvers than the state-of-the-art Grobner basis-based solvers. The proposed method can be fully automated and incorporated into existing tools for automatic generation of efficient polynomial solvers. It provides a competitive alternative to popular Grobner basis-based methods for minimal problems in computer vision. We also study the conditions under which the minimal solvers generated by the state-of-the-art action matrix-based methods and the proposed extra polynomial resultant-based method, are equivalent. Specifically we consider a step-by-step comparison between the approaches based on the action matrix and the sparse resultant, followed by a set of substitutions, which would lead to equivalent minimal solvers.