  Several popular approaches to 3D vision tasks process multiple views of the
input independently with deep neural networks pre-trained on natural images,
achieving view permutation invariance through a single round of pooling over
all views. We argue that this operation discards important information and
leads to subpar global descriptors. In this paper, we propose a group
convolutional approach to multiple view aggregation where convolutions are
performed over a discrete subgroup of the rotation group, enabling, thus, joint
reasoning over all views in an equivariant (instead of invariant) fashion, up
to the very last layer. We further develop this idea to operate on smaller
discrete homogeneous spaces of the rotation group, where a polar view
representation is used to maintain equivariance with only a fraction of the
number of input views. We set the new state of the art in several large scale
3D shape retrieval tasks, and show additional applications to panoramic scene
classification.

Computer Vision and Pattern Recognition