  The question of representation of 3D geometry is of vital importance when it
comes to leveraging the recent advances in the field of machine learning for
geometry processing tasks. For common unstructured surface meshes
state-of-the-art methods rely on patch-based or mapping-based techniques that
introduce resampling operations in order to encode neighborhood information in
a structured and regular manner. We investigate whether such resampling can be
avoided, and propose a simple and direct encoding approach. It does not only
increase processing efficiency due to its simplicity - its direct nature also
avoids any loss in data fidelity. To evaluate the proposed method, we perform a
number of experiments in the challenging domain of intrinsic, non-rigid shape
correspondence estimation. In comparisons to current methods we observe that
our approach is able to achieve highly competitive results.
