We study geometric reconstruction problems in one-dimensional retina vision. In such problems, the scene is modeled as a 2D plane, and the camera sensor produces 1D images of the scene. Our main contribution is an efficient method for computing the global optimum to the structure and motion problem with respect to the L norm of the reprojection errors. One-dimensional cameras have proven useful in several applications, most prominently for autonomous vehicles where they are used to provide inexpensive and reliable navigational systems. Previous results on one-dimensional vision are limited to the classification and solving of minimal cases, bundle adjustment for finding local optima and linear algorithms for algebraic cost functions. In contrast, we present an approach for finding globally optimal solutions with respect to the L norm of the angular reprojection errors. We show how to solve intersection and resection problems as well as the problem of simultaneous localization and mappi...