Consistent Spherical Parameterization

Many applications benefit from surface parameterization, including texture mapping, morphing, remeshing, compression, object recognition, and detail transfer, because processing is easier on the domain than on the original irregular mesh. We present a method for simultaneously parameterizing several genus-0 meshes possibly with boundaries onto a common spherical domain, while ensuring that corresponding user-highlighted features on each of the meshes map to the same domain locations. We obtain visually smooth parameterizations without any cuts, and the constraints enable us to directly associate semantically important features such as animal limbs or facial detail. Our method is robust and works well with either sparse or dense sets of constraints. Figure 1: By spherically embedding a set of models such that corresponding user-provided features map to the same spherical locations, we enable applications such as principal component analysis as shown on the right. Stretch efficiencies (left to right): 0.847, 0.795, 0.879, 0.884, 0.827.