Abstract
Many computer vision applications require robust estimation of the underlying geometry, in terms of camera motion
and 3D structure of the scene. These robust methods often
rely on running minimal solvers in a RANSAC framework.
In this paper we show how we can make polynomial solvers
based on the action matrix method faster, by careful selection of the monomial bases. These monomial bases have
traditionally been based on a Grobner basis for the poly- ¨
nomial ideal. Here we describe how we can enumerate all
such bases in an efficient way. We also show that going beyond Grobner bases leads to more efficient solvers in many ¨
cases. We present a novel basis sampling scheme that we
evaluate on a number of problems