  Estimation of differential geometric quantities in discrete 3D data
representations is one of the crucial steps in the geometry processing
pipeline. Specifically, estimating normals and sharp feature lines from raw
point cloud helps improve meshing quality and allows us to use more precise
surface reconstruction techniques. When designing a learnable approach to such
problems, the main difficulty is selecting neighborhoods in a point cloud and
incorporating geometric relations between the points. In this study, we present
a geometric attention mechanism that can provide such properties in a learnable
fashion. We establish the usefulness of the proposed technique with several
experiments on the prediction of normal vectors and the extraction of feature
lines.
