  The impressive performance of deep convolutional neural networks in
single-view 3D reconstruction suggests that these models perform non-trivial
reasoning about the 3D structure of the output space. Recent work has
challenged this belief, showing that, on standard benchmarks, complex
encoder-decoder architectures perform similarly to nearest-neighbor baselines
or simple linear decoder models that exploit large amounts of per-category
data. However, building large collections of 3D shapes for supervised training
is a laborious process; a more realistic and less constraining task is
inferring 3D shapes for categories with few available training examples,
calling for a model that can successfully generalize to novel object classes.
In this work we experimentally demonstrate that naive baselines fail in this
few-shot learning setting, in which the network must learn informative shape
priors for inference of new categories. We propose three ways to learn a
class-specific global shape prior, directly from data. Using these techniques,
we are able to capture multi-scale information about the 3D shape, and account
for intra-class variability by virtue of an implicit compositional structure.
Experiments on the popular ShapeNet dataset show that our method outperforms a
zero-shot baseline by over 40%, and the current state-of-the-art by over 10%,
in terms of relative performance, in the few-shot setting.
