  User generated 3D shapes in online repositories contain rich information
about surfaces, primitives, and their geometric relations, often arranged in a
hierarchy. We present a framework for learning representations of 3D shapes
that reflect the information present in this meta data and show that it leads
to improved generalization for semantic segmentation tasks. Our approach is a
point embedding network that generates a vectorial representation of the 3D
points such that it reflects the grouping hierarchy and tag data. The main
challenge is that the data is noisy and highly variable. To this end, we
present a tree-aware metric-learning approach and demonstrate that such learned
embeddings offer excellent transfer to semantic segmentation tasks, especially
when training data is limited. Our approach reduces the relative error by
$10.2\%$ with $8$ training examples, by $11.72\%$ with $120$ training examples
on the ShapeNet semantic segmentation benchmark, in comparison to the network
trained from scratch. By utilizing tag data the relative error is reduced by
$12.8\%$ with $8$ training examples, in comparison to the network trained from
scratch. These improvements come at no additional labeling cost as the meta
data is freely available.
