  Deep learning has achieved remarkable results in 3D shape analysis by
learning global shape features from the pixel-level over multiple views.
Previous methods, however, compute low-level features for entire views without
considering part-level information. In contrast, we propose a deep neural
network, called Parts4Feature, to learn 3D global features from part-level
information in multiple views. We introduce a novel definition of generally
semantic parts, which Parts4Feature learns to detect in multiple views from
different 3D shape segmentation benchmarks. A key idea of our architecture is
that it transfers the ability to detect semantically meaningful parts in
multiple views to learn 3D global features. Parts4Feature achieves this by
combining a local part detection branch and a global feature learning branch
with a shared region proposal module. The global feature learning branch
aggregates the detected parts in terms of learned part patterns with a novel
multi-attention mechanism, while the region proposal module enables locally and
globally discriminative information to be promoted by each other. We
demonstrate that Parts4Feature outperforms the state-of-the-art under three
large-scale 3D shape benchmarks.
