Multiple-View Geometry Under the {$L_infty$}-Norm

This paper presents a new framework for solving geometric structure and motion problems based on the L_infin-norm. Instead of using the common sum-of-squares cost function, that is, the L₂-norm, the model-fitting errors are measured using the L_infin-norm. Unlike traditional methods based on L₂, our framework allows for the efficient computation of global estimates. We show that a variety of structure and motion problems, for example, triangulation, camera resectioning, and homography estimation, can be recast as quasi-convex optimization problems within this framework. These problems can be efficiently solved using second-order cone programming (SOCP), which is a standard technique in convex optimization. The methods have been implemented in Matlab and the resulting toolbox has been made publicly available. The algorithms have been validated on real data in different settings on problems with small and large dimensions and with excellent performance.