Suboptimal Solutions to the Algebraic-Error Line Triangulation

Abstract

Line triangulation, a foundational problem in computer vision, is to estimate the 3D line position from a set of measured image lines with known camera projection matrices. Aiming to improve the triangulation's efficiency, in this work, two algorithms are proposed to find suboptimal solutions under the algebraic-error optimality criterion of the Plucker line coordinates. In these proposed algorithms, the algebraic-error optimality criterion is reformulated by the transformation of the Klein constraint. By relaxing the quadratic unit norm constraint to six linear constraints, six new single-quadric-constraint optimality criteria are constructed in the new formulation, whose optimal solutions can be obtained by solving polynomial equations. Moreover, we prove that the minimum algebraic error of either the first three or the last three of the six new criteria is not more than $\sqrt{3}$ times of that of the original algebraic-error optimality criterion. Thus, with three new criteria and all the six criteria, suboptimal solutions under the algebraic error minimization and the geometric error minimization are obtained. Experimental results show the effectiveness of our proposed algorithms.