Triangulation of Points , Lines and

The problem of reconstructing 3D scene features from multip le views with known camera motion and given image correspondences is considered. This is a classical and one of the most basic geometric problems in computer vision and photogramm etry. Yet, previous methods fail to guarantee optimal reconstructions they are either plagued by local minima or rely on a non-optimal cost-function. A common framework for the tri angulation problem of points, lines and conics is presented. We define what is meant by an opt imal triangulation based on statistical principles and then derive an algorithm for com puting the globally optimal solution. The method for achieving the global minimum is based on conve x and concave relaxations for both fractionals and monomials. The performance of the meth od is evaluated on real image data.