  We address the problem of estimating the alignment pose between two models
using structure-specific local descriptors. Our descriptors are generated using
a combination of 2D image data and 3D contextual shape data, resulting in a set
of semi-local descriptors containing rich appearance and shape information for
both edge and texture structures. This is achieved by defining feature space
relations which describe the neighborhood of a descriptor. By quantitative
evaluations, we show that our descriptors provide high discriminative power
compared to state of the art approaches. In addition, we show how to utilize
this for the estimation of the alignment pose between two point sets. We
present experiments both in controlled and real-life scenarios to validate our
approach.
