3DViewGraph: Learning Global Features for 3D Shapes from A Graph of
Unordered Views with Attention
Abstract
Learning global features by aggregating information over multiple views has been shown to be effective for 3D shape analysis. For view aggregation
in deep learning models, pooling has been applied
extensively. However, pooling leads to a loss of the
content within views, and the spatial relationship
among views, which limits the discriminability of
learned features. We propose 3DViewGraph to resolve this issue, which learns 3D global features
by more effectively aggregating unordered views
with attention. Specifically, unordered views taken around a shape are regarded as view nodes on
a view graph. 3DViewGraph first learns a novel
latent semantic mapping to project low-level view
features into meaningful latent semantic embeddings in a lower dimensional space, which is spanned by latent semantic patterns. Then, the content and spatial information of each pair of view
nodes are encoded by a novel spatial pattern correlation, where the correlation is computed among
latent semantic patterns. Finally, all spatial pattern
correlations are integrated with attention weights
learned by a novel attention mechanism. This further increases the discriminability of learned features by highlighting the unordered view nodes
with distinctive characteristics and depressing the
ones with appearance ambiguity. We show that
3DViewGraph outperforms state-of-the-art methods under three large-scale benchmarks