  Lifting methods allow to transform hard variational problems such as
segmentation and optical flow estimation into convex problems in a suitable
higher-dimensional space. The lifted models can then be efficiently solved to a
global optimum, which allows to find approximate global minimizers of the
original problem. Recently, these techniques have also been applied to problems
with values in a manifold. We provide a review of such methods in a refined
framework based on a finite element discretization of the range, which extends
the concept of sublabel-accurate lifting to manifolds. We also generalize
existing methods for total variation regularization to support general convex
regularization.
