  While object semantic understanding is essential for most service robotic
tasks, 3D object classification is still an open problem. Learning from
artificial 3D models alleviates the cost of annotation necessary to approach
this problem, but most methods still struggle with the differences existing
between artificial and real 3D data. We conjecture that the cause of those
issue is the fact that many methods learn directly from point coordinates,
instead of the shape, as the former is hard to center and to scale under
variable occlusions reliably. We introduce spherical kernel point convolutions
that directly exploit the object surface, represented as a graph, and a voting
scheme to limit the impact of poor segmentation on the classification results.
Our proposed approach improves upon state-of-the-art methods by up to 36% when
transferring from artificial objects to real objects.
