  Object classification with 3D data is an essential component of any scene
understanding method. It has gained significant interest in a variety of
communities, most notably in robotics and computer graphics. While the advent
of deep learning has progressed the field of 3D object classification, most
work using this data type are solely evaluated on CAD model datasets.
Consequently, current work does not address the discrepancies existing between
real and artificial data. In this work, we examine this gap in a robotic
context by specifically addressing the problem of classification when
transferring from artificial CAD models to real reconstructed objects. This is
performed by training on ModelNet (CAD models) and evaluating on ScanNet
(reconstructed objects). We show that standard methods do not perform well in
this task. We thus introduce a method that carefully samples object parts that
are reproducible under various transformations and hence robust. Using graph
convolution to classify the composed graph of parts, our method significantly
improves upon the baseline.
