  Unsupervised learning of global features for 3D shape analysis is an
important research challenge because it avoids manual effort for supervised
information collection. In this paper, we propose a view-based deep learning
model called Hierarchical View Predictor (HVP) to learn 3D shape features from
unordered views in an unsupervised manner. To mine highly discriminative
information from unordered views, HVP performs a novel hierarchical view
prediction over a view pair, and aggregates the knowledge learned from the
predictions in all view pairs into a global feature. In a view pair, we pose
hierarchical view prediction as the task of hierarchically predicting a set of
image patches in a current view from its complementary set of patches, and in
addition, completing the current view and its opposite from any one of the two
sets of patches. Hierarchical prediction, in patches to patches, patches to
view and view to view, facilitates HVP to effectively learn the structure of 3D
shapes from the correlation between patches in the same view and the
correlation between a pair of complementary views. In addition, the employed
implicit aggregation over all view pairs enables HVP to learn global features
from unordered views. Our results show that HVP can outperform state-of-the-art
methods under large-scale 3D shape benchmarks in shape classification and
retrieval.
