  In view-based 3D shape recognition, extracting discriminative visual
representation of 3D shapes from projected images is considered the core
problem. Projections with low discriminative ability can adversely influence
the final 3D shape representation. Especially under the real situations with
background clutter and object occlusion, the adverse effect is even more
severe. To resolve this problem, we propose a novel deep neural network, View
Discerning Network, which learns to judge the quality of views and adjust their
contributions to the representation of shapes. In this network, a Score
Generation Unit is devised to evaluate the quality of each projected image with
score vectors. These score vectors are used to weight the image features and
the weighted features perform much better than original features in 3D shape
recognition task. In particular, we introduce two structures of Score
Generation Unit, Channel-wise Score Unit and Part-wise Score Unit, to assess
the quality of feature maps from different perspectives. Our network aggregates
features and scores in an end-to-end framework, so that final shape descriptors
are directly obtained from its output. Our experiments on ModelNet and ShapeNet
Core55 show that View Discerning Network outperforms the state-of-the-arts in
terms of the retrieval task, with excellent robustness against background
clutter and object occlusion.
