  How to obtain the desirable representation of a 3D shape is a key challenge
in 3D shape retrieval task. Most existing 3D shape retrieval methods focus on
capturing shape representation with different neural network architectures,
while the learning ability of each layer in the network is neglected. A common
and tough issue that limits the capacity of the network is overfitting. To
tackle this, L2 regularization is applied widely in existing deep learning
frameworks. However,the effect on the generalization ability with L2
regularization is limited as it only controls large value in parameters. To
make up the gap, in this paper, we propose a novel regularization term called
Gram regularization which reinforces the learning ability of the network by
encouraging the weight kernels to extract different information on the
corresponding feature map. By forcing the variance between weight kernels to be
large, the regularizer can help to extract discriminative features. The
proposed Gram regularization is data independent and can converge stably and
quickly without bells and whistles. Moreover, it can be easily plugged into
existing off-the-shelf architectures. Extensive experimental results on the
popular 3D object retrieval benchmark ModelNet demonstrate the effectiveness of
our method.
